Autonomous underwater vehicle (AUV) is one of the most important exploration tools in the ocean underwater environment, whose movement is realized by the underwater thrusters, however, the thruster fault happens frequently in engineering practice. Oc...
A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation sys...
Reliable fault detection and diagnostics are crucial in order to ensure efficient operations in industrial assets. Data-driven solutions have shown great potential in various fields but pose many challenges in Prognostics and Health Management (PHM)...
Condition-based hydraulic cylinder maintenance necessitates quantitative fault diagnostics. However, existing methods are characterized by either qualitative or limited quantitative capabilities. In this paper, a quantitative fault diagnostic method...
The study focused on the development of -gas turbine full- and part-load operation diagnostics. The gas turbine performance model was developed using commercial software and validated using the engine manufacturer data. Upon the validation, fouling,...
Hydropower generation is one of the most prominent renewable sources of power. Run-of-river hydropower is like traditional hydropower but has significantly less environmental impact. Faults in industrial processes are a cause for large amounts of los...
The field of smart health monitoring, intelligent fault detection and diagnosis is expanding dramatically in order to maintain successful operation in many engineering applications. Considering possible fault scenarios that can occur in a system, ind...
Electrical motors and drives are the unseen forces driving our modern world, powering everything from electric vehicles to industrial machinery. The efficiency, precision, and sustainability of these systems are very important. Unexpected motor failu...
Bayesian Network (BN) models are being successfully applied to improve fault diagnosis, which in turn can improve equipment uptime and customer service. Most of these BN models are essentially trained using quantitative data obtained from sensors. Ho...
Intelligent fault diagnostics based on deep learning provides a favorable guarantee for the reliable operation of equipment, but a trained deep learning model generally has low prediction accuracy in cross-domain diagnostics. To solve this problem, a...
Smart manufacturing systems are being advocated to leverage technological advances that enable them to be more resilient to faults through rapid diagnosis for performance assurance. In this paper, we propose a co-simulation approach for engineering d...
For a sensor fault diagnostic system of aircraft engines, the health performance degradation is an inevitable interference that cannot be neglected. To address this issue, this paper investigates an integrated on-line sensor fault diagnostic scheme f...
Considering the importance of continually improving the algorithms in aircraft engine diagnostic systems, the present paper proposes and benchmarks a gas-path monitoring and diagnostics framework through the Propulsion Diagnostic Methodology Evaluati...
In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbo...
Abrupt speed variations and motor start-ups have been pointed out as critical challenges in the framework of fault diagnostics in induction motor drives, namely inter-turn short circuit faults. Generally, abrupt accelerations influence the typical sy...
This paper addresses the diagnostics of stator faults in three-phase line-start permanent magnet synchronous motors. More traditional fault diagnostic methodologies are unable to properly diagnose stator inter-turn short-circuit faults in three-phase...
The quantum alternating operator ansatz (QAOA) and constrained quantum annealing (CQA) restrict the evolution of a quantum system to remain in a constrained space, often with a dimension much smaller than the whole Hilbert space. A natural question w...
The operation and maintenance (O&M) issues of offshore wind turbines (WTs) are more challenging because of the harsh operational environment and hard accessibility. As sudden component failures within WTs bring about durable downtimes and signifi...
This paper indicates the importance and advantages of the application of hybrid models in the control of water supply systems. A range of possibilities provided by this scientific approach is presented in the practical examples related to the fault d...
Performance testing of components and subsystems of buildings is a promising practice for increasing energy efficiency and closing gaps between intended and actual performance of buildings. A typical shortcoming of performance testing is the difficul...
In this paper, a brushless motor with a three-phase inverter is investigated under healthy and multiple open-circuit faults. The occurrence of faults in an inverter will lead to atypical characteristics in the current measurements. This is why many u...
Due to numerous advantages, a neutral point clamped (NPC) inverter is a preferred choice for high-power applications and renewable technology. The reliability of the NPC inverter is a major concerning factor during the assessment of system performanc...
Root cause fault diagnostic reasoning is critical for ensuring the safety and reliability of airborne systems, where timely and accurate fault isolation can significantly reduce downtime and maintenance costs. However, the inherent complexity of faul...
This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed informatio...
The usage of Lithium-ion (Li-ion) batteries has increased significantly in recent years due to their long lifespan, high energy density, high power density, and environmental benefits. However, various internal and external faults can occur during th...
The worldwide electricity supply network has recently experienced a huge rate of solar photovoltaic penetration. Grid-connected photovoltaic (PV) systems range from smaller custom built-in arrays to larger utility power plants. When the size and shar...
As train loads and travel speeds have increased over time, railway axle bearings have become critical elements which require more efficient non-destructive inspection and fault diagnostics methods. This paper presents a novel and adaptive procedure b...
Higher requirements have been placed on fault detection for continuously operating machines in modern factories. Manual inspection faces challenges related to timeliness, leading to the emergence of autonomous diagnostic robots. To overcome the safet...
Machine-learning diagnostic systems are widely used to detect abnormal conditions in electrical equipment. Training robust and accurate diagnostic systems is challenging because only small databases of abnormal-condition data are available. However,...
Condition monitoring (CM) in the energy industry is limited by the lack of pre-classified data about the normal and/or abnormal plant states and the continuous evolution of its operational conditions. The objective is to develop a CM model able to: (...
The article concerns the problem of capacitor diagnosis in a hybrid aircraft. Capacitors are one of the most commonly damaged components of electrical vehicle drive systems. The result of these failures is an increase in voltage ripple. Most known an...
This study introduces particle filtering (PF) for the tracking and fault diagnostics of complex process systems. In process systems, model equations are often nonlinear and environmental noise is non-Gaussian. We propose a method for state estimation...
Centrifugal pumps are the fundamental components of most industries. They are used in almost every industry to transfer liquid through pipes. The breakdown of a pump causes heavy production losses, and hence, the development of an economical and user...
The utilization of hydrogen fuel in gas turbines brings significant changes to the thermophysical properties of flue gas, including higher specific heat capacities and an enhanced steam content. Therefore, hydrogen-fueled gas turbines are susceptible...
This paper presents a MATLAB-based tutorial to conduct fault diagnosis of a rolling element bearing. While there have been so many new developments in this field, no studies have addressed the tutorial aspects in this field to help the engineers lear...
Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings’ ene...
Automated fault detection and diagnostics analysis in commercial building systems using machine learning (ML) can improve the building’s efficiency and conserve energy costs from inefficient equipment operation. However, ML can be challenging t...
Broken rotor bar (BRB) faults are one of the most common faults in induction motors (IM). One or more broken bars can reduce the performance and efficiency of the IM and hence waste the electrical power and decrease the reliability of the whole mecha...
Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundan...
This paper presents the results of research and development of capacitive-based sensors of rotating shaft vibration for fault diagnostic systems of powerful turbines and hydro generators. It showed that diagnostic systems with special sensors are the...
Monitoring gearbox health is essential in industrial systems, where undetected faults can result in costly downtime and severe equipment damage. While vibration-based diagnostics are widely utilized for fault detection, analyzing large-scale, non-sta...
In modern power distribution networks, robust and intelligent fault management techniques are increasingly important as system complexity grows with the integration of distributed energy resources. This article reviews the use of deep learning method...
Energy consumption in buildings is a significant cost to the building’s operation. As faults are introduced to the system, building energy consumption may increase and may cause a loss in occupant productivity due to poor thermal comfort. Resea...
We propose to use engineering models for Bayesian Network (BN) learning for fault diagnostics at the factory-level using key performance indicators (KPIs) such as overall equipment effectiveness (OEE). OEE is widely used in industry and it measures s...
The automated fault detection and diagnostics (AFDD) of heating, ventilation, and air conditioning (HVAC) using data mining and machine learning models have recently received substantial attention from researchers and practitioners. Various models ha...
Gas-path diagnostics is an essential part of gas turbine (GT) condition-based maintenance (CBM). There exists extensive literature on GT gas-path diagnostics and a variety of methods have been introduced. The fundamental limitations of the convention...
The paper concerns a significant problem in the diagnostics of industrial processes, which is the need to achieve high fault distinguishability. High distinguishability results in the generation of precise diagnoses that enable making appropriate sec...
Dry-type air-core reactors (DARs) often have inter-turn short circuit (ITSC) faults. However, traditional fault detection methods for DARs generally demonstrate poor timeliness and low sensitivity, and few methods combine intelligent algorithms for o...
The share of photovoltaic (PV) power generation in the energy mix is increasing at a rapid pace with dramatically increasing capacity addition through utility-scale PV power plants globally. As PV plants are forecasted to be a major energy generator...
The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains,...
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