Search Results (30)

Traditional fault detection methods, often designed for centralized or cloud-based systems, are ill-suited for the edge. The deployment of predictive maintenance solutions on ultra-low-cost embedded platforms remains a significant challenge due to strict limitations in memory, processing capacity, and energy availability. To address these challenges, vibration and motor current signals were analyzed using an ultra-low-cost RP2040 microcontroller. For fault detection, this study uses statistical time-domain features and principal component analysis (PCA), followed by classification with eXtreme Gradient Boosting (XGBoost) models distilled for resource-constrained deployment. Experimental evaluation demonstrated that vibration-based features achieved a diagnostic accuracy of 94.1%, while current-based representations obtained 95.5% accuracy when using principal components, compared to 83.2% with handcrafted statistical features. Model distillation reduced memory footprint by up to 2.5× (from 0.42 MB to 0.15 MB) without compromising diagnostic fidelity, enabling deployment within the 264 KB RAM and 2 MB Flash constraints of the RP2040 microcontroller. This study proposes a modular framework that systematically evaluates statistical features, dimensionality reduction, sensor synchronization, and model distillation, thereby identifying the most cost-efficient combination of techniques that balances diagnostic accuracy with strict memory and processing constraints. The findings establish that accurate fault detection can be realized directly on severely resource-limited hardware, thereby extending the practical applicability of condition monitoring to cost-sensitive industrial environments. Full article

Autonomous ground vehicles (AGVs) are of major importance in exploration missions since they perform difficult tasks in changing or harmful environments. Mapping and exploration is crucial in hazardous areas, or areas inaccessible to humans, demanding autonomous navigation. This paper proposes a lightweight, low-cost AGV platform, which will be used in resource-constrained situations and aimed at scenarios like exploration missions (e.g., cave interiors, biohazard environments, or fire-stricken buildings) where there are serious security threats to humans. The proposed system relies on simple ultrasonic sensors when navigating and applied traversal algorithms (e.g., BFS, DFS, or $A^{*}$) during path planning. Since on-board microcomputers have limited memory, the traversal data and direction decisions are stored in a file located on an SD card, which supports long-term, energy-saving navigation and risk-free backtracking. A fish-eye camera set on a servo motor captures three photos ordered from left to right and stores them on the SD card for further off-line processing, integrating each frame into a low-frame-rate video. Moreover, when the battery level falls below 50%, the exploration path does not extend further and the AGV returns to the base station, thus combining a secure backtracking procedure with energy-efficient decisions. The resultant platform is low-cost, modular, and efficient at augmenting; thus it is suitable for exploring missions with applications in search and rescue, educational robotics, and real-time applications in low-infrastructure environments. Full article

This study presents a novel switchable multi-inlet Building integrated photovoltaic/thermal (BIPV/T) curtain wall system designed to enhance solar energy utilization in commercial buildings. The system integrates controllable air inlets and motorized dampers that dynamically adjust airflow patterns in response to real-time environmental conditions such as solar irradiance, ambient air temperature, and PV panel temperature. A steady-state energy balance model, developed using a thermal network analogy and implemented in Python, was used to simulate winter operation in Montréal, Canada. Three operating modes with different air inlet configurations were assessed to evaluate system performance across variable air velocities and solar conditions. Results indicate that the switchable system improves combined thermal and electrical generation by 2% to 25% compared to fixed one- or two-inlet systems. Under low irradiance and air velocity, one-inlet operation is dominant, while higher solar gain and airflow favor two-inlet configurations. The system demonstrates effective temperature control and enhanced energy yield through optimized airflow management. This work highlights the potential of integrated control strategies and modular façade design in improving the efficiency of solar building envelope systems and offers practical implications for scalable deployment in energy-efficient, heating-dominated climates. Full article

Aiming at the problems of low thermal analysis efficiency and high computational cost of traditional computational fluid dynamics (CFD) methods for modular fault-tolerant permanent magnet synchronous motors (MFT-PMSMs) under complex working conditions, this paper proposes a fast modeling and calculation method of motor temperature field based on a high-efficiency and high-precision network algorithm. In this method, the physical structure of the motor is equivalent to a parameterized network model, and the computational efficiency is significantly improved by model partitioning and Fourth-order Runge Kutta method. The temperature change of the cooling medium is further considered, and the temperature rise change of the motor at different spatial positions is effectively considered. Based on the finite element method (FEM), the space loss distribution under rated, single-phase open circuit and overload conditions is obtained and mapped to the thermal network nodes. Through the transient thermal network solution, the rapid calculation of the temperature rise law of key components such as windings and permanent magnets is realized. The accuracy of the thermal network model was verified by using fluid-structure coupling simulation and prototype test for temperature analysis. This method provides an efficient tool for thermal safety assessment and optimization in the motor fault-tolerant design stage, especially for heat capacity check under extreme conditions and fault modes. Full article

This paper presents the design and development of a new modular wall-climbing robot—Modular Wall Climbing-1 (MC-1)—for solving the problem of autonomous wall switching observed in wall-climbing robots. Each modular robot is capable of independently adhering to vertical surfaces and maneuvering, making it a fully autonomous robotic system. Multiple modules of MC-1 are connected by an electromagnet-based magnetic attachment method, and wall transitions are achieved using a servo motor mechanism. Moreover, an ultrasonic sensor is employed to measure the unknown wall-inclination angle. Mechanical analysis is conducted for MC-1 at rest individually and in combination to determine the required suction force. Experimental investigations are performed to assess the robot’s crawling ability, loading capacity, and wall-transition performance. The results demonstrate that the MC-1 robot is capable of multi-angle wall transitions for executing multiple tasks. It provides a new approach for wall-climbing robots to collaborate during wall transitions through a quick attachment-and-disassembly device and an efficient wall detection method. Full article

Multi-thrust solid rocket motors are extensively used in tactical missiles. To effectively achieve the desired multi-thrust performance curve, firstly, the concept of modular grain is introduced. Star grain, slot grain, and end-burning grain are chosen as the fundamental templates, which can be flexibly combined to form an arbitrary multi-thrust performance curve. Secondly, a quadric approximation of the burning perimeter is derived, leading to the establishment of a governing equation for modular grain design. This equation ensures a close match between the resulting performance curve and the target one. Thirdly, the Nelder–Mead optimization algorithm is employed to maximize the propellant loading fraction and reduce the combustion chamber size. Finally, the method successfully produces single-thrust, dual-thrust, and triple-thrust grains. The results show that the relative maximum deviation between the designed and target pressure curves is less than 6.1%. Additionally, the best grain configuration is identified, which maximizes the propellant loading fraction while adhering to the throat-to-port ratio constraints. Consequently, the concept of modular grain offers a valuable approach for creating complex internal ballistic characteristics by combining simpler grain templates. This approach allows for fast, responsive motor conceptual design, prototyping, testing, and even production, thereby advancing the development of solid rocket motors in a more efficient and effective manner. Full article

The motor drive system is pivotal for vehicles, particularly in new energy applications. However, conventional hybrid systems, which combine generator sets and single batteries in parallel configurations, fail to meet the operational demands of large pure electric mining dump trucks under fluctuating power requirements—such as high reserve power during acceleration and robust energy recovery during braking. Traditional single-motor configurations struggle to balance low-speed, high-torque operations and high-speed driving within cost-effective ranges, often necessitating oversized motors or multi-gear transmissions. To address these challenges, this paper proposes a dual-motor symmetric powertrain configuration with a seven-speed gearbox, tailored to the extreme operating conditions of mining environments. By integrating a high-speed, low-torque motor and a low-speed, high-torque motor through dynamic power coupling, the system optimizes energy utilization while ensuring sufficient driving force. The simulation results under extreme conditions (e.g., 33% gradient climbs and heavy-load downhill braking) demonstrate that the proposed configuration achieves a peak torque of 267 kNm (200% improvement over single-motor systems) and a system efficiency of 92.4% (vs. 41.7% for diesel counterparts). Additionally, energy recovery efficiency reaches 85%, reducing energy consumption to 4.75 kWh/km (83% lower than diesel trucks) and life cycle costs by 38% (USD 5.34/km). Field tests in open-pit mines validate the reliability of the design, with less than a 1.5% deviation in simulated versus actual performance. The modular architecture supports scalability for 60–400-ton mining trucks, offering a replicable solution for zero-emission mining operations in high-altitude regions, such as Tibet’s lithium mines, and advancing global efforts toward carbon neutrality. Full article

The development of low-frequency and low-amplitude wave energy harvesters has been limited by the lack of an affordable scientific evaluation platform, due to the high cost and land requirements of ground-based water channels. A 3D-printed modular wave generator, combined with the commercially available laboratory-sized wave channel, is proposed to address this. A stepper motor and an Arduino are employed as the driving source and controller. This system utilises motor parameters, such as rotational speed and number of travelled steps, to accurately control generated wave frequency and amplitude. By minimising costs and enhancing sustainability through 3D printing technology, only minor modifications are needed to adapt it to different water tank dimensions. The system can generate stable waves with frequencies from 1 Hz to 2 Hz and amplitudes from 1.5 cm to 7.1 cm under the current setting. The generated wave frequency and amplitude can be further customised by selecting faster stepper motors, as demonstrated in this study. Full article

Background: Focal mechanical vibration therapy has gained attention as a potential intervention to improve motor function while decreasing spasticity and pain in post-stroke patients. Despite promising results, there remains variability in study designs and outcomes, warranting a review of its clinical efficacy. Methods: A review was conducted to evaluate randomized controlled trials (RCTs) investigating the effects of focal mechanical vibration therapy on post-stroke rehabilitation. Six studies were included, assessing outcomes such as spasticity reduction (using the Modified Ashworth Scale), motor function recovery (Wolf Motor Function Test, Fugl-Meyer Assessment), and pain management (Visual Analog Scale, Numerical Rating Scale). The quality of studies was evaluated using the PEDro scale and RoB-2 tool. An overview review was conducted to provide a comprehensive analysis of the topic. Results: The included studies demonstrated significant reductions in spasticity and improvements in motor function in most patients receiving focal vibration therapy. Notable improvements were observed when focal vibration was combined with other rehabilitation techniques, such as progressive modular rebalancing or robotic rehabilitation. Pain levels were also reduced in several studies. However, differences in vibration parameters (frequency, amplitude), small sample sizes, and short follow-up periods limit the generalizability of the findings. Conclusions: Focal mechanical vibration therapy appears to be an effective adjunct in post-stroke rehabilitation, particularly for reducing spasticity and improving motor function. Although short-term benefits are promising, further research is required to determine long-term efficacy and optimal treatment parameters. This review evaluates the effectiveness of focal vibration therapy in treating motor deficits and spasticity in post-stroke patients. The results suggest its potential to improve these conditions, though further studies with larger sample sizes are needed to confirm its long-term efficacy. Full article

A significant challenge in utilizing hydrogen in conventional internal combustion engines is achieving a balance between NOx emissions and brake power output. A lean premixed charge (Lambda ≈ 2.5) allows for efficient and stable combustion with minimal NOx emissions. However, this comes at the cost of reduced power density due to the higher air requirements of the thermodynamic process. While supercharging can mitigate this drawback, it introduces increased complexity, cost, and size. An intriguing alternative is the 2-stroke cycle, particularly in an opposed piston (OP) configuration. This study presents the virtual development of a single-cylinder 2-stroke OP engine with a total displacement of 0.95 L, designed to deliver 25 kW at 3000 rpm. Thanks to its compact size, high thermal efficiency, robustness, modularity, and low manufacturing cost, this engine is intended for use either as an industrial power unit or in combination with electric motors in hybrid vehicles. The overarching goal of this project is to demonstrate that internal combustion engines can offer a practical and cost-effective alternative to hydrogen fuel cells without significant penalties in terms of efficiency and pollutant emissions. The design of this novel engine started from scratch, and both 1D and 3D CFD simulations were employed, with particular focus on optimizing the cylinder’s geometry and developing an efficient low-pressure injection system. The numerical methodology was based on state-of-the-art commercial codes, in line with established engineering practices. The numerical results indicated that the optimized engine configuration slightly surpasses the target performance, achieving 29 kW at 3000 rpm, while maintaining near-zero NOx emissions (<20 ppm) and high brake thermal efficiency (~40%) over a wide power range. Additionally, the cost of this engine is projected to be lower than an equivalent 4-stroke engine, due to fewer components (e.g., no cylinder head, poppet valves, or camshafts) and a lighter construction. Full article

This study presents a numerical method for evaluating the maintainability of a dual-axis solar tracking system that can be deployed in residential areas for improved energy production. The purpose of this research manuscript is threefold. It targets the following objectives: (i) First, we present the construction of a self-sufficient dual-axis solar tracking system based on a low-power electronic schematic that requires only one motor driver to control the azimuth and elevation angles of the photovoltaic (PV) panel. The automated system’s main electronic equipment comprises 1 × Arduino Mega2560 microcontroller unit (MCU), 1 × TB6560 stepper driver module, 2 × stepper motors, 2 × relay modules, 1 × solar charge controller, 1 × accumulator, and 1 × voltage convertor. Additional hardware components such as photoresistors, mechanical limit switches, rotary encoders, voltage, and current sensors are also included to complete the automation cycle of the solar tracking system. (ii) Second, the Arduino Mega 2560 prototyping board is replaced by a custom-made and low-cost application-specific printed circuit board (ASPCB) based on the AVR controller. The MCU’s possible fault domain is then further defined by examining the risks of the poor manufacturing process, which can lead to stuck-at-0 (Sa0) and stuck-at-1 (Sa1) defects. Besides these issues, other challenges such as component modularity, installation accessibility, and hardware failures can affect the automated system’s serviceability. (iii) Third, we propose a novel set of maintenance-oriented metrics that combine the previously identified variables to provide a maintainability index (MI), which serves as a valuable tool for evaluating, optimizing, and maintaining complex systems such as solar tracking devices. The experimental data show that the computed MI improves the system’s maintainability and enhances repair operations, increasing uptime. Full article

Multi-motor servo systems are widely used in industrial control. However, the single-core microprocessor architecture based on the microcontroller unit (MCU) and digital signal processor (DSP) is not well suited for high-performance multi-motor servo systems due to the inherent limitations in computing performance and serial execution of code. The bus-based distributed architecture formed by interconnecting multiple unit controllers increases system communication complexity, reduces system integration, and incurs additional hardware and software costs. Field programmable gate array (FPGA) possesses the characteristics of high real-time performance, parallel processing, and modularity. A single FPGA can integrate multiple motor servo controllers. This research uses MCU + FPGA as the core to realize high-precision multi-axis real-time control, combining the powerful performance of the MCU processor and the high-speed parallelism of FPGA. The MCU serves as the central processor and facilitates data interaction with the host computer through the controller area network (CAN). After data parsing and efficient computation, MCU communicates with the FPGA through flexible static memory controller (FSMC). A motor servo controller intellectual property (IP) core is designed and packaged for easy reuse within the FPGA. A 38-axis micro direct current (DC) motor control system is constructed to test the performance of the IP core and the heterogeneous embedded platforms. The experimental results show that the designed IP core exhibits robust functionality and scalability. The system exhibits high real-time performance and reliability. Full article

Light electric vehicles are best suited for city and suburban settings, where top speed and long-distance travel are not the primary concerns. The literature concerning light electric vehicle powertrain design often overlooks the influence of the associated driving missions. Typically, the powertrain is initially parameterized, established, and then evaluated with an ex-post-performance assessment using driving cycles. Nevertheless, to optimize the size and performance of a vehicle according to its intended mission, it is essential to consider the driving cycles right from the outset, in the powertrain design. This paper presents the design of an electric powertrain for multipurpose light electric vehicles, focusing on the motor, battery, and charging requirements. The powertrain design optimization is realized from the first stages by considering the vehicle’s driving missions and operational patterns for multipurpose usage (transporting people or goods) in European urban environments. The proposed powertrain is modular and scalable in terms of the energy capacity of the battery as well as in the electric motor shaft power and torque. Having such a possibility gives one the flexibility to use the powertrain in different combinations for different vehicle categories, from L7 quadricycles to light M1 vehicles. Full article

A modular approach to the construction of electric machines, drive systems, power supply systems is a new direction of modern technology development. Especially, the modular approach is promising for electric vehicles due to such positive aspects as increased efficiency, fault tolerance, overall reliability, safety, enhanced control capabilities, etc. In this work, the modular approach is comprehensively applied to an EV powertrain system, which includes a dual three-phase (DTP) BLDC motor with two machine modules of an asymmetric configuration, two battery modules and a supercapacitor module (SCM). The proposed H–H configuration of modular EV powertrain system includes four voltage source inverters that combine the power modules with the open ends of the windings (OEW) of the module machine armature, and provide control of their operation. Based on the developed mode system of the OEW machine module operation for EV traction and braking, a general control algorithm for the proposed configuration of the modular EV powertrain system has been developed. It combines the control of the operating modes with the functions of maintaining the required SOC level of the SCM and equalizing the SOCs of the two battery modules. The conducted simulation and experimental studies confirmed the workability and effectiveness of the proposed solutions. Full article

Under the umbrella of assistive technologies research, a lot of different platforms have appeared since the 1980s, trying to improve the independence of people with severe mobility problems. Those works followed the same path coming from the field of robotics trying to reach users’ needs. Nevertheless, those approaches rarely arrived on the market, due to their specificity and price. This paper presents a new prototype of an intelligent wheelchair (IW) that tries to fill the gap between research labs and market. In order to achieve such a goal, the proposed solution balances the criteria of performance and cost by using low-cost hardware and open software standards in mobile robots combined together within a modular architecture, which can be easily adapted to different profiles of a wide range of potential users. The basic building block consists of a mechanical chassis with two electric motors and a low-level electronic control system; driven by a joystick, this platform behaves similar to a standard electrical wheelchair. However, the underlying structure of the system includes several independent but connected nodes that form a distributed and scalable architecture that allows its adaptability, by adding new modules, to tackle autonomous navigation. The communication among the system nodes is based on the controller area network (CAN) specification, an extended standard in industrial fields that have a wide range of low-cost devices and tools. The system was tested and evaluated in indoor environments and by final users in order to ensure its usability, robustness, and reliability; it also demonstrated its functionality when navigating through buildings, corridors, and offices. The portability of the solution proposed is also shown by presenting the results on two different platforms: one for kids and another one for adults, based on different commercial mechanical platforms. Full article