A Review of BLDC Motors: Types, Application, Failure Modes and Detection

Abstract

Brushless DC (BLDC) motors are widely used in many engineering fields such as transportation, industrial automation, pumping systems, household devices, and renewable energy applications. Their popularity mainly arises from advantages like high power density, low noise, long service life, and high efficiency. This study contributes to the literature by comprehensively addressing the types, applications, faults, and diagnostic methods of BLDC motors. This review systematically examines recent studies to identify and classify common mechanical, electrical, magnetic, thermal, and sensor-related faults. Diagnostic approaches reported in these studies are then analyzed and compared. The methods are grouped into several categories, including signal processing, model-based, data driven, artificial intelligence-supported, and thermal or magnetic monitoring techniques. The review results show that hybrid and intelligent diagnostic strategies, which combine different analysis methods, significantly improve the accuracy of fault detection and enable earlier fault identification. These improvements also contribute to higher reliability and safer operation of BLDC systems. In the discussion, attention is given to the growing use of artificial intelligence and data fusion in fault diagnosis. These trends are likely to guide the next generation of condition monitoring systems for BLDC motors. Overall, this study emphasizes the importance of developing reliable and sustainable diagnostic frameworks to enhance energy efficiency and system performance. The results can provide a useful reference for researchers and engineers working on BLDC motor technologies.

1. Introduction

Electric machines, which emerged during the Industrial Revolution, have become a cornerstone of technological advancement. Among these machines, BLDC motors have gained importance in both academic studies and industrial applications in recent years [1]. BLDC motors have attracted attention because of their longer life, higher efficiency, and lower maintenance-demanding structures compared to conventional brushed motors [2]. These features have led to their widespread use in a wide variety of applications, from automotive to defense industries and household appliances to medical devices [3].

The production of these motors, which offer numerous advantages, has been supported by engineering approaches inspired by nature. Rotational motion mechanisms in biological systems attract attention with their high energy efficiency, low friction in fluid environments, and high maneuverability, forming an important reference for engineering designs [4]. These similarities demonstrate that movement systems in nature can serve as a source of inspiration for innovative motor designs in engineering. The bacterial flagellum is the best-known example of this structure, as it is a biological nanomotor driven by a proton gradient at the cellular level; however, it is not the only example [5]. Flow-directing mechanisms in fish fins, aerodynamic structures that create micro-vortices in dragonfly wings, and helical vane systems that enable some plant seeds to float in the air for long periods are also among nature’s successful rotor designs in terms of efficient rotation and thrust generation [6].

The application of these biological principles to engineering systems has yielded particularly interesting results in rotor designs at both micro and macro scales. The literature shows that biomimetic airfoils have been developed to increase the efficiency of propellers used in unmanned aerial vehicles (UAVs), demonstrating that micro-indented surfaces like bird feathers reduce aerodynamic losses, while fish-fin-shaped wing tips reduce vortex-induced inefficiency [7]. Studies have revealed that rotor blade profiles inspired by maple seeds and similar helical seed geometries provide high thrust and stable rotation behavior even at low speeds, demonstrating that biological inspiration directly contributes to engineering performance [8]. Another study shows that biomimetic propellers offer advantages such as noise reduction, vibration reduction, increased gliding efficiency, and higher lift force with lower energy consumption, particularly in multi-rotor UAVs [9].

Therefore, these aerodynamic and hydrodynamic advantages provided by biological systems are not merely a conceptual source of inspiration; they find direct application in UAVs equipped with BLDC motors, micro-robotic systems, small powerful electric propulsion systems, and rotor architectures operating at low Reynolds numbers. Notable results in this field include the increased stability of multi-rotor propulsion structures that mimic multi-tail logic, the high thrust produced by helical blade geometries even at low speeds, and the improved boundary layer behavior of surface textures in microstructures [10].

In this context, there is a striking similarity between the working principle of the bacterial flagellum and the electromagnetic drive mechanism of the BLDC motor. In this context, Figure 1A,B show the bacterial flagellum mechanism, whereas Figure 1C shows the different BLDC types (inner and external rotor BLDC types). The fundamental principle in both systems is the conversion of energy into rotational motion, torque generation, and optimization of fluid motion. Therefore, biologically inspired rotor and propeller designs are gaining importance as a noteworthy research area in UAVs, micro air vehicles, robotic systems, and low-noise electric propulsion applications using BLDC motors.

This similarity is not merely a theoretical analogy; in recent years, interest in nature-inspired designs has increased in engineering. Advantages such as low noise, high lift efficiency, and reduced friction in fluids, observed in biological structures, are being adapted to rotor and propeller systems driven by BLDC motors. The studies in the literature focus on multi-segmented and aerodynamically optimized rotor structures like bacterial flagella [11,12,13]. This biomimetic approach has created an important research area aimed at improving BLDC motor performance in both micro-flying vehicles and UAVs.

Figure 1. Similarity between the biological motor structure of the bacterial flagellum and the engineering structure of the BLDC motor; (A,B) the bacterial flagellum mechanism, (C) different BLDC types [4,5].

Figure 1. Similarity between the biological motor structure of the bacterial flagellum and the engineering structure of the BLDC motor; (A,B) the bacterial flagellum mechanism, (C) different BLDC types [4,5].

The main reasons for the widespread use of BLDC motors are their technical advantages, such as high efficiency, compact structure, quiet operation, low maintenance, high torque-to-weight ratio, and high dynamic performance [4,5]. Owing to their permanent magnet rotor structure, elimination of the need for brushes and commutators greatly reduces energy losses and mechanical wear caused by friction [14,15]. These structural advantages make BLDC motors stand out in areas such as electric vehicles (EVs), unmanned aerial vehicles, robotic systems, and the aerospace and defense industries [11].

Nevertheless, BLDC motors are known to have certain structural and functional faults despite these technological benefits. Particular faults that can negatively impact motor performance and system reliability include torque ripple, electromagnetic interference (EMI), acoustic noise, electronic commutation faults, rotor and stator faults, bearing faults, sensor faults, and thermal instability [16]. Therefore, it is necessary to enhance both the motor design and control algorithms. To address these faults, advanced drive techniques, such as vector control, direct torque control, and artificial intelligence-based control, have been extensively studied in the literature [17,18,19].

The increasing use of BLDC motors has necessitated more careful evaluation of potential faults in these motors. In particular, in motors operating at high speeds and under harsh conditions, intrinsic faults such as stator and rotor faults can cause serious system faults if not detected early [20]. Therefore, developing fault diagnosis systems for monitoring the health status of motors and early detection of faults are of great importance. In the literature, the methods developed for this purpose are generally classified into three main groups: signal-based, model-based, and data-based approaches [21,22,23]. Among these methods, motor current signature analysis, vibration analysis, acoustic emission measurements, artificial neural networks, support vector machines, and Bayesian classifiers stand out [24].

In recent years, more reliable and accurate fault detection systems have been developed by combining multi sensor data with machine learning algorithms [22]. The features obtained from vibration and current signals are transferred to artificial intelligence models, and possible faults in both the electrical and mechanical components of the motor can be classified with high accuracy [25]. In such studies, third harmonic analysis, kurtosis, and statistical measurements can be used to make detailed inferences about the magnitude and type of failure [25,26].

BLDC motors are classified according to their structure. There are two main structures: inner rotor and outer rotor types [27]. While inner rotor motors are preferred in applications requiring high speed and low torque, outer rotor motors are used in systems requiring low speed and high torque [28,29]. There are also structural differences between systems that use sensors to determine the rotor position and those that operate without sensors. Although sensorless systems offer advantages in terms of cost and simplicity, they have disadvantages, such as the inability to accurately determine the rotor position at low speeds [30]. This necessitates the development of special techniques, such as initial position detection.

Modeling and simulation of BLDC motors are also important for the development of control and fault detection systems. Methods used for estimating motor parameters include estimation error methods, continuous-time system identification algorithms, and Fourier transforms [31]. These models have been successfully used in both simulation and real-time microcontroller-based applications [32,33]. In addition, some studies have shown that these models, which are implemented in low-cost embedded systems, yield effective results in field applications [34].

This study aims to comprehensively evaluate the structural characteristics of BLDC motors, motor types, common failure types, and diagnostic methods for these failures by examining the relevant literature with a disciplined and holistic approach. The main objective of the study is to eliminate the fragmented nature of existing research, present the information systematically and coherently, and clarify technical concepts that are often confused in the literature. In this regard, the study reexamines the existing knowledge on both the mechanical and electrical components of BLDC motors through a detailed analysis, discussing failure mechanisms along with their effects and presenting a holistic perspective on the advantages and limitations of different diagnostic approaches. Furthermore, in light of the gaps identified in the literature, technical requirements and potential areas for development that could guide future research are discussed, highlighting where BLDC motor technology needs to advance in terms of academic research and industrial applications. In this respect, the study aims to provide a framework of reference for researchers in the field, focusing on both theory and practice.

2. BLDC Motor Types

BLDC motors are widely used in many sectors, such as automotive, aerospace, medical, and industrial automation, owing to their advantages, such as high efficiency, low maintenance, and long life. The structural diversity of BLDC motors has led to the emergence of specially developed types according to the requirements of different application areas [35]. In this section, the structural classifications of BLDC motors, types according to the number of phases, effects of the number of poles on the performance, and preferred motor types according to the application areas are discussed in detail.

2.1. Structural Classification

2.1.1. Inrunner BLDC Motors

In the inner rotor design, a rotating magnet rotor is located at the center of the fixed stator windings. This structure, thanks to its low rotor inertia, allows the motor to reach high speeds quickly. Because the heat source is located in the stator, cooling is more effective. This design is especially preferred for radio control vehicles, high-speed fans, and EVs. Figure 2 shows the structure of the internal rotor BLDC motor.

Figure 2. Inner rotor BLDC motor structure [36].

Figure 2. Inner rotor BLDC motor structure [36].

2.1.2. Outrunner BLDC Motors

In the outer rotor design, the stator is located in the center of the motor, while the rotor magnets are located in the outer shell and rotate around the stator. This structure provides a higher torque arm and more inertia, allowing for a more uniform torque production at low speed [35,37]. The propeller drive is used in applications requiring high torque, such as pump systems [38]. Figure 3 shows the structure of an external rotor BLDC motor, while Figure 4 shows a comparison of internal rotor and external rotor BLDC motors (1–5) in terms of scores.

Figure 3. Outer rotor BLDC motor structure [36].

Figure 3. Outer rotor BLDC motor structure [36].

Figure 4. Comparison of inner and outer rotor BLDC motor [36].

Figure 4. Comparison of inner and outer rotor BLDC motor [36].

When examining the structural characteristics and performance parameters of BLDC motors, it is evident that the fundamental factor underlying motor selection is the requirements specific to the operating scenario. This is because different application areas bring with them different mechanical and electromagnetic design priorities. In this context, the rotor configuration of the motor, whether an outer rotor or inner rotor, is a critical design decision specific to the application.

Table 1. Areas of application for external and internal rotor motors in various industrial applications.

Field of Application	Recommended Structure	Reason
Drones, Model Aircraft	Outer Rotor	High starting torque, stable operation at low speed
Industrial Robots	Inner Rotor	Fast response, low inertia, suitable for precise control
Electric Scooters, E-Bikes	Outer Rotor	High torque and compact design required; hub-type drive motor configurations are commonly used
CNC Machine Tools, Servo Systems	Inner Rotor	Supports closed-loop control systems and offers high-speed control accuracy
Fans, Cooling Systems	Outer Rotor	Stable operation at low speed, low noise level
Medical Equipment	Inner Rotor	Low noise level, high control precision
Power Tools	Outer Rotor	High starting torque, stable operation at low speed, low noise level

presents a comparative assessment of the applications and recommended structural types of outer and inner rotor motors in various industrial applications.

This comparison demonstrates that application-focused motor selection is based not only on electromagnetic efficiency but also on multi-dimensional criteria such as mechanical integration, thermal management, and compatibility with control systems.

2.2. BLDC Motors According to Phase Number

The number of phases is a parameter that directly affects the control structure and performance characteristics of BLDC motors. Motors are generally classified as single-phase and three-phase based on their phase structure. However, it is used in multi-phase motors in some recent studies.

2.2.1. Single Phase BLDC Motors

Single-phase BLDC motors generally have simpler construction and are used in low-power applications [39]. These motors usually consist of four stator arms and one phase winding. Due to their simple structure, they are low in cost, but they can only rotate in one direction, and their torque production is limited [40,41,42]. They are widely preferred for small fans, simple pump systems, and household appliances [43]. Figure 5 shows the basic structure of a single-phase BLDC motor.

Figure 5. Basic structure of single-phase BLDC motor [40].

Figure 5. Basic structure of single-phase BLDC motor [40].

2.2.2. Three Phase BLDC Motors

Three-phase BLDC motors have three winding groups, each with a 120° phase difference [44]. Thanks to this structure, the rotor can rotate both clockwise and anticlockwise. In addition, torque generation is more uniform, and torque ripple is very low [45]. They are widely used in industrial automation, robotic systems, CNC machines, and EVs [43]. Figure 6 shows the basic structure of a three-phase BLDC motor. In

Table 2. Comparison of single-phase and three-phase BLDC motors.

Feature	Single Phase	Three Phase
Structure	Simple	Complex
Direction Control	One Way	Two-Way
Torque Generation	Low	Medium-High
Torque Surge	High	Low
Area of Use	Fans, Pumps, Small Devices	EV, CNC, Robotic Systems

, the characteristics of single-phase and three-phase motors are compared.

Figure 6. Basic structure of three-phase BLDC motor [40].

Figure 6. Basic structure of three-phase BLDC motor [40].

2.3. BLDC Motors According to Number of Poles

In BLDC motors, the number of poles of the magnets on the rotor has a direct effect on the torque production and maximum speed of the motor [46]. A higher pole number provides more frequent commutation, resulting in higher torque, but this reduces the motor’s maximum speed [47]. It is critical to select the appropriate number of poles according to the application. Figure 7 shows the electronic commutation of BLDC motors and Table 3 shows the effect of pole number on performance.

Figure 7. Electronic commutation of BLDC motors [40].

Figure 7. Electronic commutation of BLDC motors [40].

Table 3. Effect of number of poles on performance.

Number of Poles	Torque Generation	Maximum Speed	Application Example
2	Low	High	RC vehicle, drone
4	Center	Center	Servo systems
6	High	Low	Fan, industrial drive

Table 3. Effect of number of poles on performance.

Number of Poles	Torque Generation	Maximum Speed	Application Example
2	Low	High	RC vehicle, drone
4	Center	Center	Servo systems
6	High	Low	Fan, industrial drive

BLDC motors offer higher efficiency, low maintenance and long life compared to conventional brushed DC motors. Thanks to their structural diversity (inner rotor/outer rotor), number of phases, and pole arrangements, there is an optimal solution for almost every application. Three-phase motors with an inner rotor are preferred in systems requiring high speed and precise control, while motors with an outer rotor are preferred in applications requiring high torque at low speed. Single-phase motors are ideal for simple, low-power systems. In motor selection, the combination of torque, speed, control complexity, cost, and physical layout criteria is critical for system performance and efficiency [48].

3. Industrial Applications for BLDC Motors

The fundamental criteria for selecting a BLDC motor are the target torque and speed range, the operating environment of the system, energy efficiency expectations, and ease of control [49]. In the context of industrial applications, motors with a high number of poles are typically favored for their ability to generate high torque at low speeds. Conversely, designs with a lower number of poles are often preferred in applications necessitating high speed and precise speed control [50]. However, it is imperative to acknowledge the significance of power density, thermal management capacity, and driver topology in the selection process.

As demonstrated in Figure 8, BLDC motors have a wide range of applications. The operational parameters of the motor, such as continuous operation or short-term high-load requirements, noise requirements, energy consumption constraints, and cost factors, are considered based on the intended application. While motors that can operate efficiently at high power and a wide speed range are preferred in EVs, minimizing vibration and acoustic noise is paramount in medical devices [51]. Consequently, the selection process for BLDC motors should be evaluated not solely based on nominal speed and torque values but also on the operating cycle, driver algorithms, and the system’s overall performance criteria.

It can thus be concluded that the application areas of BLDC motors are directly related not only to the mechanical performance of the motor but also to the specific requirements of the application area. In this context, the motor selection process is determined by a general evaluation of sector-specific parameters and operating conditions.

Figure 8. Applications of BLDC motors [52].

Figure 8. Applications of BLDC motors [52].

3.1. Transportation Systems

According to data from the International Energy Agency (IEA) [53], the number of EVs in active use worldwide is projected to exceed 17 million by 2025, and this figure is expected to rise to over 55 million when including plug-in hybrid vehicles. The BLDC motor is one of the most widely employed motor types in EV powertrains. It is evident that these motors are particularly favored in light electric vehicles (LEVs). The aforementioned factors can be attributed to three key elements: namely, high efficiency, low maintenance requirements, and compact design. The prevalence of BLDC motors, especially in the field of electric scooters, can be attributed to their capacity for direct integration into the wheel and their ability to provide regenerative braking function. As demonstrated in [53], the integration of hub BLDC motor technology has been shown to enhance the back electromotive force performance of light EVs by approximately 3%, as reported in [52,53,54]. However, it should be noted that these motors also have disadvantages. Power concentration in the wheel hub has been demonstrated to exert a detrimental effect on the mass distribution of the vehicle, thereby compromising stability. Additionally, the continuous and smooth production of torque is rendered challenging. Finally, it has been determined that hub motors are subject to elevated mechanical stress in comparison to conventional BLDC motors [54,55].

As demonstrated in [47], BLDC motors employed in EVs are designed to comply with international efficiency standards IE-2, IE-3, and IE-4. These motors are typically powered by 24–48 V battery packs and operate with batteries ranging from 40 Ah to 100 Ah in capacity [52,56,57]. However, there is still no international standardization regarding motor controllers, and manufacturers generally develop customized control strategies and hardware.

Another effective application area for BLDC motors is in the context of drones. In the context of drones, the high power-to-weight ratio, the fast response capability due to the low moment of inertia, and the quiet operation of BLDC motors make them indispensable [50]. The utilization of these motors in the propulsion systems of both multi-rotor unmanned aerial vehicles and fixed-wing platforms is a key feature of their design. Considering its direct relationship with flight duration, it is imperative to acknowledge the pivotal role played by motor efficiency in drones. Consequently, the investigation into the lightweight design and energy efficiency of BLDC motors assumes paramount significance [58,59,60]. Consequently, BLDC motor technology occupies a pivotal position in the primary propulsion systems of EVs, as well as in scooters, drones, and other mobile applications [61]. Moreover, BLDC motors are extensively utilized in auxiliary automotive subsystems, including turbocharging systems, HVAC units, and seat comfort systems [62].

As illustrated in Table 4, a comparative overview of the motor types, operating voltages, battery capacities, and motor powers of various EVs used worldwide is presented [63]. This comparison indicates that BLDC motors are progressively becoming a technological standard, not only in land vehicles but also in micro-mobility and aviation applications. Furthermore, a comparative analysis of the merits and drawbacks of BLDC motors compared to other motor types is provided in Table 5 [64]. As demonstrated in Table 5, the ‘0’ rating denotes the mean performance of motor types with respect to the specified criterion. The ‘−’ and ‘−−’ ratings indicate a disadvantage compared to other motor types with regard to the specified criterion, while the ‘+’ and ‘++’ ratings indicate advantage compared to other motor types regarding the relevant criterion.

Table 4. Types of motors used in EVs [59,60,61,62,63,64].

Brand/Model	Country	Motor Type	Power (kW)	Drive Type	Year
Ather 450	India	BLDC	5.4	Hub motor	2018
Bajaj Chetak	India	BLDC	4	Hub motor	2020
Revolt RV400	India	BLDC	4	Hub motor	2019
Artisan es-1 pro	UK	Advanced PM	11	Mid-drive	2020
Nissan Leaf	Japan	PMSM	110	FWD	2020
Tesla Model 3	USA	ACIM	204	RWD	2020
Porsche Taycan 4S	Germany	ACIM	320	4WD	2020
BMW i3	Germany	PMSM	125	RWD	2019
Hyundai Kona Electric	South Korea	PMSM	150	FWD	2020
Audi e-Tron 55 Quattro	Germany	ACIM	265	4WD	2019
Toyota Prius	Japan	PMSM	53	FWD	2019

Table 4. Types of motors used in EVs [59,60,61,62,63,64].

Brand/Model	Country	Motor Type	Power (kW)	Drive Type	Year
Ather 450	India	BLDC	5.4	Hub motor	2018
Bajaj Chetak	India	BLDC	4	Hub motor	2020
Revolt RV400	India	BLDC	4	Hub motor	2019
Artisan es-1 pro	UK	Advanced PM	11	Mid-drive	2020
Nissan Leaf	Japan	PMSM	110	FWD	2020
Tesla Model 3	USA	ACIM	204	RWD	2020
Porsche Taycan 4S	Germany	ACIM	320	4WD	2020
BMW i3	Germany	PMSM	125	RWD	2019
Hyundai Kona Electric	South Korea	PMSM	150	FWD	2020
Audi e-Tron 55 Quattro	Germany	ACIM	265	4WD	2019
Toyota Prius	Japan	PMSM	53	FWD	2019

Table 5. Selection criteria for different types of electric motors [65].

Criterion	ACIM	PMSM	BLDC
Cost	+	−	+
Efficiency	++	++	++
Simplicity	+	0	+
Reliability	++	++	++
Size	+	++	++
Overload Capacity	++	+	+
Robustness	++	++	+
Power Density	++	++	++
Fault Tolerance	−	+	++
Thermal Limit	++	+	−
Torque Ripple	0	−	−−
Lifetime	++	++	++

Table 5. Selection criteria for different types of electric motors [65].

Criterion	ACIM	PMSM	BLDC
Cost	+	−	+
Efficiency	++	++	++
Simplicity	+	0	+
Reliability	++	++	++
Size	+	++	++
Overload Capacity	++	+	+
Robustness	++	++	+
Power Density	++	++	++
Fault Tolerance	−	+	++
Thermal Limit	++	+	−
Torque Ripple	0	−	−−
Lifetime	++	++	++

3.2. Industry and Manufacturing

BLDC motors are also widely used in numerous industrial applications, such as automation robots, cranes, lifts, conveyor belt systems, and CNC machines. In static applications within this field, BLDC motors are advantageous due to their ability to produce precise torque without fluctuations. Furthermore, BLDC motors have low inertia, high-power density, and stable operation across a wide speed range, ensuring high efficiency in industrial production lines. However, BLDC motors do have certain technical limitations in industrial conditions. These include overheating during continuous operation at high torque, electromagnetic interference from power electronics, and the need to control torque–flux fluctuations [65]. Maintaining the magnetic properties of the rotor magnets over the long term is critical for motor longevity, particularly in motors operating under heavy loads.

In recent years, various control strategies have been developed to address these faults. In some systems, field-oriented control algorithms are used to create a more balanced torque production structure [66]. Other approaches include direct torque control methods, which provide a high dynamic response, and adaptive control algorithms, which increase the motor’s ability to adapt to variable load conditions [67]. Additionally, advanced cooling systems and the use of magnet materials that can withstand high temperatures are important steps towards increasing motor reliability. While BLDC motors offer advantages such as high precision, energy efficiency, and ease of maintenance in industrial automation, continuous improvements are needed to minimize faults such as heating, EMI, and torque fluctuations [68]. Therefore, improvements in motor design and control algorithms directly impact system efficiency and reliability in industrial applications.

3.3. Pumping and Flow Systems

BLDC motors are widely used in water pumping and flow systems due to their high efficiency and energy-saving properties. The most significant advantage of these motors is their ability to provide constant torque and stable speed, even under variable load conditions [69]. Furthermore, their low maintenance requirements and long service life make them a more suitable option than traditional induction motors. One of the most significant developments in BLDC motors for pumping applications has been their integration with renewable energy sources [70,71]. When used with photovoltaic systems, it has become possible to pump water independently of the electricity grid. In such systems, the motor is powered directly by the energy obtained from solar panels, and MPPT algorithms increase panel efficiency, enabling continuous pumping throughout the day.

3.4. Home Appliances

Traditionally, single-phase induction motors have been widely used in household appliances. However, due to their low efficiency, these motors lead to high energy consumption and fail to meet the increasingly important energy efficiency standards of today [72,73]. Consequently, there is a need to develop motor systems that consume less energy, are environmentally friendly, and offer users long-term cost advantages. In this context, BLDC motors stand out for domestic use thanks to their superior features, including energy efficiency, quiet operation, and high durability. Using BLDC motors in washing machines, dishwashers, refrigerators, water pumps, fans, and air conditioning systems reduces energy consumption and enhances device performance [74]. Furthermore, motor control units enable a better power factor and ensure system stability. Another advantage of using BLDC motors in household appliances is reduced acoustic noise and stable operation over a wider speed range [75]. This significantly increases user comfort, particularly in devices such as air conditioners, fans, and vacuum cleaners. Moreover, digital control-based drivers accelerate motor response, reduce energy losses, and extend the service life of devices.

Market analyses indicate that BLDC motors will become widespread in household appliances within the next five years [75]. Motors operating in the 500–10,000 RPM range is considered ideal for domestic use. Furthermore, internal rotor motor designs are increasingly favored over external rotor motors due to their compact structure and more balanced torque production [76]. However, despite the widespread use of BLDC motors in household appliances, some technical challenges still need to be addressed. The most significant of these are the development of fault-tolerant designs, reducing torque fluctuations, and minimizing electromagnetic interference [71]. Solutions developed to address these faults will increase device reliability and contribute to greater energy savings in the long term. BLDC motors are particularly notable in domestic applications thanks to their high efficiency, low energy consumption, and long service life. Their use is expected to become even more widespread alongside smart home systems and automation-based solutions in the future [76].

3.5. Energy and Environment

The growing demand for energy, the environmental problems linked to the use of fossil fuels, and the threat of global warming have further emphasized the importance of energy technologies that are highly efficient [77]. Electric motors, being one of the components with the largest share in total energy consumption, are a prime example of this, necessitating the development of more sustainable energy solutions. BLDC motors are an environmentally friendly solution for energy production and management, offering high efficiency, low losses and a long service life [77,78]. They provide energy savings and optimize resource use due to their low heating and maintenance requirements.

BLDC motors play a critical role in renewable energy systems. In small- and medium-scale wind turbine systems, BLDC motors are used for generator drive and rotor position control. Thanks to their ability to produce high torque at low speeds, these motors enable maximum power extraction from wind energy [78]. At the same time, fast and precise rotor orientation increases the efficiency with which energy is produced. Similarly, BLDC motors are widely used in solar tracking systems to ensure that photovoltaic panels track the sun throughout the day [79]. The motors continuously adjust the panel angle according to the position of the sun to maximize energy production. Control units enable the motors to perform precise positioning with low energy consumption, thereby guaranteeing the efficient operation of the panels [77]. These applications increase energy production and shorten the payback period of solar energy systems.

Using BLDC motors in renewable energy systems is critical not only for increasing energy efficiency, but also for reducing greenhouse gas emissions and contributing to sustainable energy production [78]. As wind turbines and solar tracking systems become more prevalent in the future, the importance of BLDC motors in the energy and environmental sectors will grow even further. As discussed above, the applications of BLDC motors in different fields clearly demonstrate their advantages in terms of energy efficiency, torque control, and system reliability. Table 6 summarizes the characteristic features and performance criteria of BLDC motors in various applications, enabling clearer comparisons of motor performance and the challenges encountered in different application areas. The next section will detail faults that directly affect BLDC motor performance.

Table 6. Characteristics and performance criteria of BLDC motors in various applications [78].

Application Area	Motor RPM	Energy Efficiency	Torque Fluctuation	EMI Effect
Home Appliances	500–10,000	High	Medium	Low
Pump and Flow Systems	1000–3000	High	Low	Medium
Wind Turbines	50–3000	Very High	Medium	Low
Solar Tracking Systems	50–500	High	Very Low	Low
Industrial Robots	500–5000	Very High	Medium	Medium
CNC Machines/Cranes	500–3000	High	Low	Medium

Table 6. Characteristics and performance criteria of BLDC motors in various applications [78].

Application Area	Motor RPM	Energy Efficiency	Torque Fluctuation	EMI Effect
Home Appliances	500–10,000	High	Medium	Low
Pump and Flow Systems	1000–3000	High	Low	Medium
Wind Turbines	50–3000	Very High	Medium	Low
Solar Tracking Systems	50–500	High	Very Low	Low
Industrial Robots	500–5000	Very High	Medium	Medium
CNC Machines/Cranes	500–3000	High	Low	Medium

3.6. Special Use Areas

BLDC motors are also important in specialized applications such as minimally invasive surgical robots, underwater vehicles, small satellites, and nanosatellites. In these applications, the motor’s compact design, high power density, and low vibration levels directly affect the surgeon’s precise manipulation and safe operation [80]. Furthermore, the motor’s low noise and thermal characteristics support operation in sterile environments and long-term operations [81]. The literature reports that BLDC motors used in surgical robots provide high torque production and repeatable motion despite mechanical miniaturization, thereby increasing the accuracy and reliability of robotic manipulators [82].

The main reasons for preferring BLDC motors in underwater vehicles are their high sealing performance, low vibration, and energy efficiency [83]. Since underwater robots typically operate in long-term missions and harsh environmental conditions, the reliability and quietness of the motor play a critical role [84]. Low vibration and noise levels improve mission performance by maintaining the accuracy of sonar and sensor systems [81,85]. The literature shows that BLDC motors operate with high efficiency in underwater robots, reduce maintenance requirements, and provide a long service life [86,87]. These motors can also be easily integrated into the limited space of underwater vehicles thanks to their compact design.

BLDC motors are also critical for orbital and position control in small satellites and nanosatellites. In these applications, the motors high-precision speed control, low energy consumption, and compact size enable compatibility with limited space and power sources [88]. Research shows that miniature BLDC motors are effectively used in satellite stabilization systems and improve orbital accuracy [89]. These motors are also preferred in such specialized applications because they operate with low vibration and electromagnetic interference levels, allowing sensitive sensors and communication systems to function without being adversely affected.

4. BLDC Motor Faults

BLDC motors are used in many different applications and experience a variety of failures throughout their lifespan for a variety of reasons. Early detection and accurate classification of these failures is critical to ensuring system reliability and availability. BLDC motor failures generally fall into one of five main categories: mechanical, magnetic, electrical, thermal, and control, sensor-related. Mechanical problems primarily result from shaft misalignment, bearing wear, or rotor deformation. Magnetic failures directly affect the motor’s torque production capacity and include failures such as broken magnets or demagnetization [90]. Electrical failures relate to short circuits in windings or insulation failures and typically cause the motor to stall suddenly or lose performance. Thermal problems result from prolonged overloading and inadequate cooling, which shorten the lifespan of motor components [91,92]. Failures related to sensors and control circuits typically manifest as failures in Hall sensors or driver boards and directly affect the motor’s stable operation. BLDC motor failures can be divided into the five groups mentioned above, and the proportional distribution of these failure types as observed in field studies is shown in Figure 9. Therefore, a detailed examination of these fault types in the reliability analysis of BLDC motors is important for developing preventive maintenance strategies and designing fault diagnosis algorithms.

Figure 9. Percentage occurrence of BLDC motor fault types [52].

Figure 9. Percentage occurrence of BLDC motor fault types [52].

4.1. Mechanical Faults

Mechanical failures are one of the most common types of failures in BLDC motors, directly impacting the motor’s reliability and long-term performance. Studies have shown that approximately 40–45% of all failures are mechanical in origin [52,93]. Consequently, mechanical failures are considered one of the most critical failures in academic research and industrial maintenance processes. The most common type of mechanical failure is bearing failure. Bearings enable the rotor to rotate with low friction and play a critical role in the efficient operation of the motor. Inadequate lubrication, environmental pollutants, high temperatures, installation errors, or prolonged operation at high speeds can shorten bearing life [94]. These types of failures typically manifest as increased vibration and noise and a decrease in the motor’s overall efficiency. In advanced stages, bearing failures can prevent the rotor from rotating freely, leading to serious system downtime or complete motor failure.

Another major cause of mechanical failures is shaft misalignment [95]. An unequal air gap between the rotor and stator leads to an uneven distribution of electromagnetic forces. This causes the rotor to become radially or axially misaligned. Shaft misalignment not only reduces motor efficiency but also increases the load on the bearings, leading to premature bearing failure. Therefore, shaft misalignment is a critical failure affecting system reliability through both direct and indirect effects. Rotor misalignment is another significant factor in mechanical failures [94,95]. Small imbalances in the rotor during production can worsen during high-speed operation, causing the motor to resonate. This increases the vibration level and causes structural fatigue in the motor housing and fasteners. In the long term, such vibration-induced loads can lead to further failures, such as cracks in the motor housing or loosening of fasteners [96].

Furthermore, magnet damage or breakage is one of the rarer but more serious types of mechanical failure [94]. Cracked or broken permanent magnets in the rotor can disrupt the motor’s balance, negatively affecting both the magnetic field distribution and the motor’s mechanical stability [97]. This can lead to erratic motor behavior at high speeds and pose serious safety risks. Structural damage caused by vibration is also classified as a mechanical failure [94,95]. Long-term operation involving periodic vibrations can cause loosening of housing connections, the formation of microcracks in the shaft, or the system to enter resonant zones. Because these types of failures usually develop slowly, sudden and unexpected failures can occur without regular maintenance and monitoring. Figure 10 illustrates the different conditions that cause mechanical failures: (a) material fatigue, (b) improper bearing seating, and (c) corrosion.

Figure 10. Different types of mechanical failure; (a) material fatigue, (b) chips and cracks resulting from incorrect bearing placement, (c) corrosion [97].

Figure 10. Different types of mechanical failure; (a) material fatigue, (b) chips and cracks resulting from incorrect bearing placement, (c) corrosion [97].

Mechanical failures are a critical problem that can shorten the lifespan of BLDC motors, reduce their efficiency, and increase safety risks. Appropriate material selection during the design process, adherence to precise manufacturing tolerances, and regular maintenance procedures are key factors in preventing these failures. Furthermore, ignoring mechanical failures can affect not only the motor itself but also the entire system to which it is connected, creating the risk of chain failure.

4.2. Electrical Faults

Studies in the literature indicate that electrical failures account for approximately 30–35% of all failures [93]. Electrical failures can directly affect the motor’s magnetic flux generation process, leading to efficiency losses, sudden stops, and even permanent damage [80]. Therefore, prevention and early detection of electrical failures is crucial for the reliable and long-lasting operation of BLDC motors. Table 7 lists the types, causes, and consequences of electrical failures.

Table 7. Types of electrical faults and the causes and consequences of these faults [96,97,98].

Type of Electrical Fault	Causes of Formation	Effects on the Motor	Results
Winding Insulation Faults	High temperature, humidity, vibration, excessive voltage	Insulation breakdown, inter-winding leakage current	Short circuit, motor stoppage, fire risk
Interphase Short Circuit	Weakening of insulation, excessive current	Torque imbalance, high vibration	Sudden motor stoppage, system failure
Overcurrent/Voltage Fluctuations	Incorrect driver parameters, harmonics, low-quality power electronics	Winding overheating, electronic circuit damage	Reduced motor life, loss of efficiency
Sensor/Electronic Circuit Faults	Hall sensor failure, driver circuit faults, semiconductor damage	Incorrect position information, loss of synchronization	Loss of motor control, system failure
Shaft Currents (Bearing Currents)	Unstable magnetic field, high-frequency switching, driver-induced harmonics	Wear and erosion on bearing surfaces	Bearing failure, increased vibration, premature loss of motor life

Table 7. Types of electrical faults and the causes and consequences of these faults [96,97,98].

Type of Electrical Fault	Causes of Formation	Effects on the Motor	Results
Winding Insulation Faults	High temperature, humidity, vibration, excessive voltage	Insulation breakdown, inter-winding leakage current	Short circuit, motor stoppage, fire risk
Interphase Short Circuit	Weakening of insulation, excessive current	Torque imbalance, high vibration	Sudden motor stoppage, system failure
Overcurrent/Voltage Fluctuations	Incorrect driver parameters, harmonics, low-quality power electronics	Winding overheating, electronic circuit damage	Reduced motor life, loss of efficiency
Sensor/Electronic Circuit Faults	Hall sensor failure, driver circuit faults, semiconductor damage	Incorrect position information, loss of synchronization	Loss of motor control, system failure
Shaft Currents (Bearing Currents)	Unstable magnetic field, high-frequency switching, driver-induced harmonics	Wear and erosion on bearing surfaces	Bearing failure, increased vibration, premature loss of motor life

Among all electrical faults, winding insulation faults are the most common [97]. Long-term operation, excessive current loads, high temperatures, and high humidity cause the insulation material in the stator windings to deteriorate over time. This deterioration can lead to short circuits between phases or leakage currents between the winding and chassis [99]. Such faults can reduce motor efficiency and cause serious safety issues, such as an increased risk of fire.

Another important type of electrical fault is shaft currents. This fault occurs particularly in applications using high-frequency switching inverters and in cases of unbalanced magnetic field distribution [100]. Capacitive and inductive coupling between the rotor and stator causes unwanted currents to flow through the motor shaft. These currents often pass through the bearing surfaces, causing micro-pitting, frosting, and surface damage. Over time, this leads to increased bearing clearance, increased vibration, and a shortened mechanical life of the motor. Shaft currents are a critical failure mechanism, particularly in EVs, wind turbine generators, and high-speed pump-submersible motors [100,101]. Insulated bearings, shaft grounding brushes, and high-quality drive filtering techniques are widely used to mitigate this problem. Therefore, preventing shaft currents extends bearing life and improves overall motor reliability. Figure 11 illustrates the damage that shaft currents can inflict on a bearing.

Figure 11. Shaft current damage on the bed; (a) surface damage, (b) icing, (c) pitting [97].

Figure 11. Shaft current damage on the bed; (a) surface damage, (b) icing, (c) pitting [97].

Another important type of electrical fault is phase-to-phase short circuits. These faults disrupt the motor’s electromagnetic balance, causing sudden increases in current values. This can lead to damage to both the windings and power electronic components. Short circuits usually occur suddenly and unpredictably and are therefore one of the most critical faults that can cause the motor to stop completely. Another type of electrical fault is faults in power electronic circuits [97]. Because BLDC motors are operated through drive circuits and control systems, faults in semiconductor elements such as inverters, MOSFETs, or IGBTs can directly affect motor operation. In particular, overcurrent, high-temperature, or sudden voltage fluctuations can cause these elements to fail. Although power electronic faults are often perceived as motor faults, they originate in the control unit, making diagnosis difficult [101,102].

Electrical faults cause increased EMI, torque fluctuations, and loss of efficiency in the system [103]. Increased EMI levels can negatively impact the motor’s performance and other surrounding electronic systems. Consequently, electrical faults in BLDC motors can cause widespread problems affecting both the motor components and the power electronic systems to which they are connected [104]. Therefore, appropriate insulation materials should be selected, cooling methods appropriate to the motor’s operating conditions should be implemented, and overcurrent and overvoltage protection circuits should be designed. Furthermore, using methods such as thermal imaging, current spectrum analysis, and model-based diagnostic algorithms for early detection of electrical faults increases motor reliability and extends system life.

4.3. Magnetic Faults

Magnetic faults in BLDC motors arise directly from their electromagnetic design and operating conditions. This leads to fluctuations in torque production and a decrease in energy efficiency [91]. The primary causes of magnetic faults are permanent magnet damage, magnetic saturation, misaligned shafts, magnet demagnetization, and magnetic imbalances. Breakage, cracking, or partial loss of magnetic properties of permanent magnets can cause a significant decrease in motor performance [91,98]. Figure 12 shows the loss of magnetic flux density and the resulting unbalanced distribution in a BLDC motor with a faulty magnet. High temperatures, excessive currents, or peak current magnitudes in the motor can cause the magnets to lose their permanent magnetism. In this case, rotor torque and motor efficiency decrease, and ripple increases. Furthermore, errors in magnet assembly or improper rotor placement can cause an unbalanced magnetic field distribution between the stator and rotor, producing asymmetric magnetic forces [105]. This increases motor vibration levels and creates additional loads on the bearings.

Magnetic faults do not just affect the rotor magnets. Stator core saturation also has a negative impact on the motor’s operating characteristics. In the saturation state, the magnetic flux density becomes nonlinear, making torque–flux control difficult [101]. This is particularly evident in industrial BLDC motors operating under high loads. To prevent such faults, it is recommended to use high-temperature-resistant rare earth magnets, perform geometric optimizations in the rotor design to minimize flux leakage, and improve motor thermal management systems. In addition, the thermal–magnetic modeling techniques developed in recent years are widely used to predict the risk of magnet demagnetization [105,106]. Therefore, these failures should be prevented by selecting appropriate materials and considering operating conditions and cooling strategies during the design phase.

Figure 12. Magnetic flux density distribution of a BLDC motor with a broken magnet [105].

Figure 12. Magnetic flux density distribution of a BLDC motor with a broken magnet [105].

4.4. Thermal Faults

Losses occurring during BLDC motor operation cause an increase in the temperature of the windings, magnets, and bearings [107]. This temperature increase occurs under conditions of inadequate cooling, high current density, and overload, leading to problems that negatively affect both motor performance and lifespan [108]. The primary causes of thermal failure are deterioration of the winding insulation, demagnetization of permanent magnets, loss of bearing lubrication, and increased magnetic losses in the stator core [109]. The temperature increase in the stator windings can damage the chemical structure of the insulation materials, causing short circuits [101]. This can cause the motor to shut down suddenly or pose a fire risk.

The permanent magnets in the rotor are also highly sensitive to high temperatures. If they exceed a certain temperature, they irreversibly lose their permanent magnetic properties. This significantly reduces the motor’s torque production capacity. Furthermore, thermal effects on the bearings cause the lubricant film to deteriorate and accelerate friction-related failures. Inadequate heat dissipation and cooling systems are another source of thermal failure. BLDC motors, especially in compact applications such as household appliances, have limited cooling surfaces. Therefore, if appropriate cooling channels, fan-assisted air-cooling systems, or liquid cooling solutions are not incorporated into the motor design, heat accumulation becomes inevitable [97]. Figure 13 shows the temperature changes when a fan-assisted air-cooling system is used with a BLDC motor, and Figure 14 shows the temperature changes when a shaver cannot provide sufficient cooling.

Figure 13. Temperature change in the fan with a BLDC motor; (a) healthy fan display, (b) thermal display of the healthy fan at 2100 rpm, (c) display of the blocked fan, (d) thermal display of the blocked fan at 2100 rpm [97].

Figure 13. Temperature change in the fan with a BLDC motor; (a) healthy fan display, (b) thermal display of the healthy fan at 2100 rpm, (c) display of the blocked fan, (d) thermal display of the blocked fan at 2100 rpm [97].

Figure 14. Temperature change in a shaver equipped with a BLDC motor; (a) thermal representation of a healthy shaver, (b) thermal representation of a shaver with blocked air outlet [97].

Figure 14. Temperature change in a shaver equipped with a BLDC motor; (a) thermal representation of a healthy shaver, (b) thermal representation of a shaver with blocked air outlet [97].

To prevent such failures, modern motor designs utilize heat-resistant insulation materials and magnets resistant to high temperatures, as well as advanced cooling strategies and continuous monitoring systems with temperature sensors [97]. Furthermore, thermal modeling and simulation techniques optimize the thermal design by predicting the temperature distribution of the motor under different loads and environmental conditions. Consequently, thermal failures are a critical factor affecting the reliability and efficiency of BLDC motors. Therefore, effective thermal management strategies must be implemented throughout the entire process, from motor design to operating conditions.

4.5. Sensor and Control Faults

BLDC motors are not limited to the types of failures mentioned above. Sensors and control systems, which directly affect motor performance, can also cause failures [110]. These motors require accurate detection of rotor position and precise control of current-torque balance [111]. Therefore, failures in sensors or control algorithms can lead to significant reductions in motor efficiency and operational reliability. Sensor failures typically occur in Hall sensors, encoders, or current-voltage sensors. Hall sensors, which generate false signals due to electromagnetic interference, can cause incorrect detection of rotor position information [112]. This can lead to commutation errors, which can lead to torque fluctuations, vibrations, and loss of motor synchronization. In encoder-based systems, mechanical vibrations or environmental factors such as dust and humidity can reduce sensor sensitivity and motor control accuracy.

Commutation errors, PWM signal distortions, software-based algorithm errors, and overheating in control circuits are the most common problems encountered in control systems [95]. Inadequate control algorithms can prevent stable motor operation, especially at high speeds, which has a negative impact on both energy efficiency and torque quality. Furthermore, faults in power electronic circuits directly threaten the stability of the control system. In recent years, sensorless control algorithms have become increasingly common to prevent sensor and control failures [113,114]. This approach involves deriving rotor position from stator currents and voltages, thus eliminating the need to obtain data from Hall sensors. This method eliminates sensor-related faults and reduces system cost and complexity. However, sensorless control methods continue to be a research topic because they can lead to loss of accuracy, especially at low speeds.

5. BLDC Motor Fault Detection Methods

Section 4 classifies faults observed in BLDC motors as mechanical, electrical, magnetic, thermal, or sensor-driven. These faults occur in different components of the motor and negatively impact performance, efficiency, and reliability. Mechanical faults are generally caused by bearing, shaft, and rotor imbalance, while electrical faults are associated with insulation failure, short circuits, and shaft currents [91]. Magnetic faults are caused by magnet breakage, cracking, or demagnetization, while thermal faults are caused by high temperatures and inadequate cooling [101]. Sensor and control faults are associated with position sensing errors, control circuit failures, or software-based commutation faults [110,111]. Table 8 lists the fault types, causes, and detection methods identified in Section 4. To improve motor reliability, it is crucial to accurately classify fault types and apply specific detection methods for each type.

The mechanical, electrical, magnetic, thermal, and sensor-related faults summarized in Table 8 represent the most common fault types encountered in BLDC motors. However, identifying these faults requires advanced diagnostic systems that combine multi-dimensional data analysis, signal processing, and AI-enabled methods, rather than relying solely on the traditional approach of physically inspecting the motor. Due to the complex structure and dynamic operating conditions of BLDC motors, each fault type generates unique frequency, vibration, and current values. This necessitates the use of multidisciplinary analysis techniques to ensure accurate and rapid fault detection.

Table 8. Types of faults in BLDC motors, their causes, and detection methods [115].

Fault Type	Cause of Formation	Symptoms	Detection Methods
Mechanical Faults	Misalignment, insufficient lubrication, overload, vibration	Increased vibration, noise, irregular rotation, excessive heating	Vibration analysis, acoustic signal analysis, speed fluctuation measurement, condition monitoring sensors
Electrical Faults	Insulation breakdown, excessive current, harmonics, sudden temperature change	Current imbalance, excessive current draw, motor failure, burning smell	Current-voltage analysis, impedance measurement, signal monitoring with oscilloscope, harmonic analysis
Magnetic Faults	High temperature, impacts, manufacturing defects, prolonged overload	Torque fluctuations, loss of efficiency, irregular speed changes	Magnetic flux measurement, Hall sensor data, FEM-based analysis, magnetic field scanning
Thermal Faults	Insufficient cooling, high current, increased ambient temperature	Insulation melting, performance decline, motor stoppage	Thermal camera measurements, temperature sensors, thermal modeling, IR thermography
Sensor and Control Faults	Sensor failure, EMI effects, software or control circuit failure	Incorrect speed/position information, irregular operation, sudden motor stoppage	Sensor signal analysis, fault code diagnosis, control circuit tests, shaft current measurement

Table 8. Types of faults in BLDC motors, their causes, and detection methods [115].

Fault Type	Cause of Formation	Symptoms	Detection Methods
Mechanical Faults	Misalignment, insufficient lubrication, overload, vibration	Increased vibration, noise, irregular rotation, excessive heating	Vibration analysis, acoustic signal analysis, speed fluctuation measurement, condition monitoring sensors
Electrical Faults	Insulation breakdown, excessive current, harmonics, sudden temperature change	Current imbalance, excessive current draw, motor failure, burning smell	Current-voltage analysis, impedance measurement, signal monitoring with oscilloscope, harmonic analysis
Magnetic Faults	High temperature, impacts, manufacturing defects, prolonged overload	Torque fluctuations, loss of efficiency, irregular speed changes	Magnetic flux measurement, Hall sensor data, FEM-based analysis, magnetic field scanning
Thermal Faults	Insufficient cooling, high current, increased ambient temperature	Insulation melting, performance decline, motor stoppage	Thermal camera measurements, temperature sensors, thermal modeling, IR thermography
Sensor and Control Faults	Sensor failure, EMI effects, software or control circuit failure	Incorrect speed/position information, irregular operation, sudden motor stoppage	Sensor signal analysis, fault code diagnosis, control circuit tests, shaft current measurement

In the literature, fault detection methods are generally classified under five main headings: signal processing techniques; model-based methods; artificial intelligence-based methods; data-based methods; and thermal and magnetic monitoring-based methods [116]. Because each approach has different advantages and limitations, it is important to choose the appropriate method depending on the fault type, system structure, and available data. This section will discuss these methods in detail, including the conditions under which faults in BLDC motors can be detected using these techniques, sample applications from the literature, and the practical effectiveness of these methods from an academic perspective.

5.1. Signal Processing Methods

One of the most widely used methods for detecting faults in BLDC motors is signal processing. These methods are based on the mathematical and statistical analysis of signals such as current, voltage, vibration, or acoustic noise obtained during motor operation [117]. Directly examining signals in the time domain provides significant advantages, especially for understanding transient behavior during sudden load changes or start-up. Using signal processing, damage to the bearing system of a BLDC motor can be observed as spikes and irregular vibrations in the time signal. The presence and severity of a fault can be assessed by calculating statistical parameters such as mean, variance, peak value, or kurtosis. This method can detect not only BLDC faults but also ESC faults in BLDC motors [118]. Figure 15 shows the setup of a data collection study for ESC fault detection.

Figure 15. Current data collection study in brushless motors used for ESC fault diagnosis [118].

Figure 15. Current data collection study in brushless motors used for ESC fault diagnosis [118].

However, analyses performed in the time domain may not always be sufficient. The motor’s frequency components provide more effective fault detection. Therefore, transferring signals to the frequency domain using Fourier transform is an effective method for diagnosing electrical faults [119]. In BLDC motors, electrical faults, such as short circuits or phase breaks in the stator windings, produce characteristic harmonic components [120]. The fast Fourier transform enables fault diagnosis by isolating these components. Furthermore, power spectral density analysis reveals noise sources or magnetic imbalances by examining the motor’s energy distribution in the frequency domain. For example, in the case of rotor shaft misalignment, prominent sideband frequencies are observed in the stator current. While these methods provide high accuracy during continuous operation, they may be limited in detecting short-term, sudden faults [121].

The Fourier transform divides the motor signal into specific time intervals and performs frequency analysis at each. This method allows us to monitor how instantaneous load changes or sudden voltage imbalances in BLDC motors evolve over time. More advanced wavelet transform techniques are particularly suitable for analyzing transient and nonlinear signals [122,123]. Short-term events such as rotor magnet failure or insulation weakening become apparent through wavelet analysis. In recent years, the Hilbert–Huang transform has also been favored for the separation of nonlinear and nonstationary motor signals [124]. This approach provides a more detailed understanding of mechanical and electrical faults by examining the motor’s complex vibration patterns.

Taken together, it is clear that signal processing techniques play an important role in BLDC motors for both early fault diagnosis and periodic maintenance planning. However, because each method has its own advantages and limitations, hybrid approaches are often preferred in practical applications [125]. When a BLDC motor is used in a compressor system, vibration signals can be initially analyzed in the time domain for preliminary assessment. Then, fault frequencies can be determined using frequency-based analysis. Finally, the transient nature of the fault can be examined in detail using the wavelet transform. This provides high reliability in detecting both permanent and transient faults.

5.2. Model-Based Methods

Model-based methods for fault diagnosis in BLDC motors involve creating a physical or mathematical model of the system and then comparing the predicted behavior with actual system data [126]. The primary goal of this approach is to create a reference model representing the normal operating conditions of the motor and use the differences between the measured data and this model as fault indicators. Consequently, the fault detection process is closely linked to understanding and modeling the system dynamics. There are generally two main approaches to model-based methods: analytical modeling and state-observer-based modeling methods [127]. Analytical modeling involves mathematically representing the motor, and the relationships between current, torque, speed, and electromotive force are usually described by differential equations [128]. Significant deviations from this mathematical model are observed when conditions such as winding short circuits, rotor magnet failures, or torque fluctuations occur in BLDC motors. Therefore, the presence and type of fault can be determined by analyzing the magnitude of the error between the measured signals and the model outputs.

In contrast, state-observer-based methods aim to estimate the system’s state variables in real time. Kalman filters, extended Kalman filters, sliding-mode observers, and Luenberger observers are frequently used for this purpose [129]. Kalman-based methods are widely preferred for fault detection because they can accurately estimate motor parameters in environments with high measurement noise [130]. Figure 16 shows the block diagram of the extended Kalman filter for BLDC motor faults. These observers estimate normal system behavior based on the motor model. A difference between the measured and predicted values exceeding a certain threshold indicates a potential fault.

Figure 16. Extended Kalman filter for BLDC motor fault detection [129].

Figure 16. Extended Kalman filter for BLDC motor fault detection [129].

In BLDC motor applications, model-based approaches are highly effective in early detection of disturbances that affect system dynamics, such as electrical faults and magnetic imbalances [126,127]. In a BLDC motor, phase current imbalances or changes in electromagnetic torque can cause deviations from model predictions, and these differences can be used to determine the magnitude and location of the fault. Furthermore, in hybrid systems where thermal models are integrated, motor parameter changes resulting from temperature increases can be monitored using model-based controllers. This allows for the prediction of faults resulting from overheating.

The main advantage of these methods is that they require a deep understanding of the physical system, enabling high-accuracy fault detection. However, this also presents a limitation, as all motor parameters must be known to obtain accurate results. Furthermore, as model complexity increases, so does the computational load, which limits processor capacity, especially in real-time applications [131]. In this context, model-based methods provide a robust theoretical framework for fault diagnosis in BLDC motors, performing particularly well in applications where the system dynamics are well understood and sensor data is reliable. Recent studies have integrated these methods with AI-based techniques to create hybrid detection systems, combining the physical accuracy of the model with the generalizability of learning-based systems [132,133].

5.3. Artificial Intelligence-Based Methods

In recent years, AI- and machine learning-based methods have increasingly replaced traditional analytical approaches in motor fault detection [132]. Classical mathematical models often fail to represent the entire behavior of such systems; therefore, data-driven and learning-based approaches offer a more flexible and adaptable solution [133]. The complex electromechanical structure of BLDC motors, with their nonlinear and time-varying dynamics, makes AI-based models more successful than other methods [134]. AI-based fault diagnosis begins with the extraction of statistical or spectral features from signals obtained during motor operation. These features are then converted into a feature vector and fed into classification, regression, or clustering algorithms [135]. Classification-based methods are the most widely used strategy for determining the fault type. Algorithms such as artificial neural networks, support vector machines, decision trees, random forests, and k-nearest neighbors offer high accuracy in distinguishing between faulty and healthy conditions [136]. Numerous studies in the literature have trained artificial neural networks based on vibration, current, and EMF signals for early diagnosis of BLDC motor faults [132,133,134,135,136]. To detect winding short circuits in a faulty BLDC motor, time-frequency-based features extracted from phase currents are fed to a neural network; the network predicts the presence of a fault based on the differences between these patterns [135,136]. Similarly (Figure 17), it classifies the fault types and determines the severity of faults using the position of the motor.

Figure 17. Block diagram of the ANN prediction algorithm for BLDC motor fault detection [136].

Figure 17. Block diagram of the ANN prediction algorithm for BLDC motor fault detection [136].

In addition to machine learning techniques, deep learning-based approaches have become increasingly prominent in recent years. Convolutional neural networks can perform fault detection directly on raw data, eliminating the need for manual feature extraction from motor signals. CNN models have a high success rate in analyzing complex signal patterns, particularly those of BLDC motors, such as magnetic imbalance and bearing failure [137,138]. Furthermore, recurrent neural networks and long short-term memory networks are effectively used to predict fault evolution by learning time-dependent patterns in time-series data. The main advantage of these methods is that they can make decisions based solely on measured data, independent of the motor’s physical parameters [136,137]. This allows the system to be easily adapted to different motor types or operating conditions. However, a disadvantage is that large and balanced datasets are required to obtain accurate results. Insufficient data can lead to a decrease in the model’s generalization ability or an increased risk of misclassification. Consequently, recent research has focused on overcoming these limitations using techniques such as transfer learning, data augmentation, and hybrid models [136,137,138]. Hybrid-based approaches increase fault detection accuracy and early diagnosis performance. The use of hybrid models in vibration-based fault detection has achieved a fault diagnosis accuracy rate of 98.8% in the literature [139]. Furthermore, the use of these hybrid models has significantly reduced early diagnosis time [137,138]. Consequently, studies are being conducted on the development of self-learning predictive maintenance systems that integrate these approaches with model-based and signal processing techniques.

5.4. Data-Based Methods

This approach performs the fault detection process based on the statistical properties of the measurement data, without directly relying on the physical model or theoretical equations of the system. The basic principle is to analyze patterns and differences between large volumes of data obtained from the healthy and faulty states of the engine and to extract fault indicators from these differences [140]. Therefore, data-based methods have an observation-based and empirical learning structure. The main advantage of these methods is that they eliminate the need for complex physical models. Because BLDC motors are nonlinear, multivariable, and dynamic systems, accurately modeling all physical parameters is both difficult and time-consuming. Data-based analysis overcomes this challenge by generating meaningful information directly from measured signals. The goal is to predict the system’s state by detecting hidden patterns, trends, or anomalies in time-series data.

The most common techniques used in data-based fault detection include statistical process control, principal component analysis, independent component analysis, cluster analysis, and correlation-based monitoring methods [140,141]. These methods identify deviations related to fault occurrence by establishing statistical relationships in motor operating signals. The power of data-based approaches lies in their potential to create early warning systems. Faults in BLDC motors typically develop gradually. Bearing damage begins with a slight increase in vibration, followed by current fluctuations and thermal instabilities. Data-based monitoring systems can provide early detection by statistically analyzing these small changes before any visible deterioration occurs. Sliding window-based analyses can dynamically assess engine health by monitoring trends in time-varying signals [142]. In these systems, continuous data streams from the engine are collected via sensors, analyzed online, and sent to decision support systems. This provides real-time information about the engine’s operating status, enabling preventive maintenance decisions in the event of potential failures.

However, the accuracy of data-based methods depends largely on the quality of the data, the sampling rate, and the reliability of the measurements [143]. Inaccurate estimates can occur due to noisy or missing data. Furthermore, processing high-dimensional data can incur significant computational costs. However, despite these drawbacks, the ability to detect fault signals directly from measurement data without the need for a physical model makes this method practical and reliable. As digital sensor technologies develop and big data infrastructures become widespread, data-based approaches are increasingly used in real-time fault diagnosis systems [144]. Consequently, many recent studies have combined data-based methods with dimensionality reduction, feature selection, and machine learning approaches to develop hybrid systems [133,134].

5.5. Thermal and Magnetic Tracking-Based Methods

Thermal analysis and imaging-based fault detection methods are techniques that assess motor health by examining the thermal reflections of electrical and mechanical faults. Abnormal operating conditions in BLDC motors often result in localized temperature increases [97]. Therefore, monitoring the temperature distribution is crucial for early fault detection and prevention. Thermal analysis evaluates the heat transfer dynamics of a BLDC motor. Under normal operating conditions, the temperature distribution on the motor surface is uniform. However, in the event of a fault, heat accumulates intensely at specific points. Partial short circuits in the stator windings of a faulty BLDC motor lead to increased current density in this region and abnormal heating. Similarly, rotor shaft misalignment or bearing failure cause mechanical losses from friction to be converted into thermal energy [145]. Such conditions not only reduce the overall efficiency of the motor but can also lead to more serious long-term consequences such as insulation degradation and magnet remanence loss.

Infrared thermography is one of the most widely used methods for detecting such thermal anomalies [97]. Surface temperature maps obtained by thermal cameras provide visual and quantitative data about the motor’s operating conditions (Figure 13 and Figure 14). Thermographic images can be used in both offline and online monitoring systems. High-speed infrared cameras, in particular, capture temperature changes on a millisecond scale, enabling the creation of early warning systems. Numerous studies in the literature have successfully detected local temperature anomalies in the stator windings, rotor magnets, and bearing areas of BLDC motors using IR thermography [144,145]. In recent years, thermal imaging-based analyses have been combined with artificial intelligence-based image processing algorithms [146]. Deep learning-based convolutional neural networks can automatically classify normal and faulty conditions by analyzing patterns obtained from thermal images. This approach offers a significant advantage, especially in detecting small temperature differences that are imperceptible to the human eye. Furthermore, the spatial characteristics of heat distribution maps are used to understand the thermal responses of motor components under different load conditions, enabling load-dependent fault detection. Thermal analysis methods provide valuable data not only for fault detection but also for planning preventive maintenance strategies. Continuous thermal monitoring systems record thermal trends throughout the motor’s operating life, enabling the prediction of potential faults before they occur. Such systems are important for protecting motors in application areas where thermal health is critical, such as industrial automation and EV motors.

Imaging-based approaches are not limited to thermal imaging. Electromagnetic field imaging and high-speed camera-based visual analysis are also used to observe the rotational stability, rotor alignment, and commutation behavior of BLDC motors [147]. When used in conjunction with thermography to detect complex faults such as mechanical vibration and rotor imbalance, these methods form the basis of multi-data fusion-based diagnostic systems. In this context, thermal analysis and imaging-based methods are powerful tools for reliable, real-time, and non-contact fault detection in BLDC motors. Methods based on both surface temperature and heat flow reveal the overall health of the system. Moreover, these methods are becoming increasingly common in sensorless systems as they provide high accuracy fault diagnosis without requiring physical contact [148].

6. Future Challenges and Opportunities

In recent years, BLDC motor technologies have shown a strong upward trend in the academic literature and in industrial applications thanks to their efficiency, ease of maintenance, and modular structure [149]. While this market currently has very high growth potential, the sustainability of this growth depends on overcoming the technical and economic obstacles facing the technology. The increasing demand for BLDC motors in sectors such as automotive, consumer electronics, drones, and automation is putting pressure on manufacturers to deliver innovative solutions while controlling costs. Figure 18 illustrates the expected BLDC motor demand by motor count across different industries in the next few years.

Figure 18. Future analysis of end-user demand for BLDC motors used in different fields [52,150].

Figure 18. Future analysis of end-user demand for BLDC motors used in different fields [52,150].

Additionally, cost pressure is one of the most serious challenges faced by BLDC motors. The cost of critical components, such as magnet raw materials and high-performance semiconductor elements, directly impact motor production costs [151]. Fluctuations in rare earth magnet prices and supply chain uncertainties are reducing manufacturers’ profit margins, particularly in highly competitive markets. This situation is accelerating the search for lower-cost materials or alternative magnet solutions. The global BLDC motor market is currently valued at approximately USD 22.2 billion, and the annual compound growth rate is expected to be around 6–7 per cent until 2030 (Figure 19). Cost optimization strategies and integrated supply chain models will be crucial for manufacturers to gain a competitive advantage in the future.

Figure 19. Market analysis of BLDC motors in the coming years [150].

Figure 19. Market analysis of BLDC motors in the coming years [150].

In addition to cost pressures, technical limitations in BLDC motor control electronics are also significant. The high-frequency switching techniques employed in motor drivers can result in electromagnetic interference, harmonic distortions, and parasitic effects. In sensitive medical devices, communication equipment or systems with strict electromagnetic compatibility standards, these faults may necessitate expensive filtering solutions. In such cases, driver design and EMI management engineering solutions become increasingly important. From a technical performance perspective, BLDC motors present challenges in high-power, low-speed combinations. In applications such as pump systems, energy storage systems or heavy industrial drives, the ability to provide high torque at low speeds can be critical [152,153]. Additionally, the motor’s internal losses and thermal loads create significant obstacles when approaching control limits. Thermal management has become one of the most important parameters in determining the performance limit of the motor, particularly in compact packaging designs or high-energy-density applications.

Overcoming these technical obstacles also presents significant opportunities in the world of BLDC motors. First, the development of control strategies has the potential to reduce cost and complexity by removing sensors from the motor system [154]. This approach works by deriving the rotor position from back-EMF or current-voltage signals and minimizes the impact of sensor errors on the system. Software-centric optimizations, adaptive control techniques with embedded microcontrollers, can improve system performance by better managing variation tolerances. Furthermore, material science and magnetic optimization studies also hold potential to shape the future of BLDC technology [154,155]. Innovations in new magnet alloys, structures that increase magnetic flux without reducing magnet volume, high thermal tolerance magnetic materials, and lightweight composite rotor–stator structures are at the forefront. Such developments provide advantages in terms of the motor’s cost-efficiency balance.

Another important area of opportunity is the use of the Internet of Things (IoT) and data-driven predictive maintenance applications. Monitoring BLDC motors using sensor networks integrated with cloud-based analysis systems enables the creation of early warning systems that can detect faults before they occur [156]. This reduces maintenance costs and increases system availability. This approach can significantly impact applications where energy efficiency and uptime are critical [157].

Table 9. Challenges and solutions in BLDC motors.

Research Area	Challenges	Solutions and Opportunities
Materials Engineering	High cost and supply risk of rare earth magnets	Ferrite-based magnets, development of composite magnetic structures
Thermal Management	Difficulty in temperature control with increasing power density	Active cooling systems, smart materials
Control Electronics	EMI and harmonic distortions	EMI filtering, space vector PWM, GaN-based inverters
Sensor Technology	Sensor faults, noise in position sensing	Sensorless control algorithms, software-based prediction
Data-Driven Systems	Lack of real-time data analysis	IoT-based predictive maintenance systems
Sustainability and Recycling	Difficulty in magnet recycling	Recyclable composite materials

summarizes the challenges and solutions for BLDC motors. In conclusion, the future of BLDC motors will be shaped by cost pressures and technical challenges. However, overcoming these challenges through sensorless control techniques, advances in materials, advanced driver designs, and data-driven analysis will enable BLDC technology to expand its scope of applications.

7. Conclusions

This study provides a comprehensive examination of the structural characteristics, application areas, potential failure modes, and detection methods of BLDC motors. BLDC motors are now widely used in applications ranging from industrial systems to EVs thanks to their high efficiency, low maintenance requirements, and precise control capabilities. However, their broad range of applications has also led to an increased focus on the complex failure types to which motors are exposed under different operating conditions. In this study, BLDC motor faults are classified into five categories: mechanical, electrical, magnetic, thermal, and sensor-control. Each type of failure is considered a critical factor that directly affects system performance and reduces energy efficiency. In this context, the advantages, limitations, and applicability conditions of traditional signal processing, model-based, data-based, and thermal analysis-imaging-based methods proposed in the literature were compared, along with the methods themselves. Thus, this study provides a comprehensive evaluation of existing fault detection strategies.

The findings suggest that a single method is insufficient for all failure scenarios. The highest success rates are achieved using hybrid approaches that combine signal processing-based feature extraction with artificial intelligence-supported classification techniques. Notably, deep learning-based methods are robust against noise and can process multiple sensor data simultaneously, making them a prominent application in this field. Real-time fault detection, data standardization, and artificial intelligence applications will continue to be priority research topics in BLDC motor fault diagnosis in the future. Furthermore, the continuous monitoring of motor data via IoT-enabled systems, coupled with the widespread adoption of early warning systems that leverage cloud-based analytics, will markedly enhance system reliability while curbing maintenance expenses. In conclusion, this study provides an interdisciplinary perspective on reliability analysis of BLDC motors and offers a guiding framework for academic research and industrial applications alike. The future of BLDC motor technologies will be shaped by hardware improvements and the integration of data science, signal processing, and artificial intelligence-based approaches. It is anticipated that future studies will extend system life, increase energy efficiency, and contribute to the development of preventive maintenance strategies.