Faults In Electrical Machines Research Articles

A New Modeling Approach and Comprehensive Monitoring of Electrical Faults Through Spectral Analysis in DSIM

The objective of this study is to evaluate the dependability of double-star induction machines by employing meticulous monitoring and intelligent handling of potential electrical malfunctions that may arise during their functioning. Specifically, the research delves into the examination of three distinct fault types: rotor bar breakage, stator phase opening, and inter-turn short circuit. Utilizing a mathematical model developed in the natural reference frame (abc), our aim is to comprehensively delineate both normal and faulty operational scenarios. To categorize and gauge the gravity of identified faults, we employ a methodology rooted in spectral analysis. In order to ensure continuous machine operation, especially in instances of stator phase opening, our proposed mitigation approach entails intentionally opening a secondary phase positioned at a 90-degree angle to the faulty phase. This strategic alteration transforms the machine into a dual two-phase configuration. Through rigorous simulation analyses, our findings underscore the efficacy and pragmatic viability of the devised fault detection technique, offering valuable insights into the domain of electrical machine reliability and fault management.

Turn‐to‐turn and phase‐to‐phase short circuit fault detection of wind turbine permanent magnet generator based on equivalent magnetic network modelling by wavelet transform approach

AbstractOne of the most common faults in electric machines is a turn‐to‐turn short circuit (TTSC), which may destroy coil insulation and demagnetise the magnet. In addition, the phase‐to‐phase short circuit (PPSC) fault, which can have even more destructive effects than the TTSC fault, is introduced and analysed. The equivalent magnetic network (EMN) method, with high modelling accuracy and a short computation time, is employed for healthy and faulty machines. The current signal under fault conditions is analysed in the dqo frame, showing the presence of the second harmonic component in its waveform. This fault detection index is processed using the signal processing technique of discrete(wavelet transform (DWT). Besides, energy analysis is used to distinguish TTSC and PPSC faults. Finally, finite element and EMN modelling results are compared with the experimental data of the prototyped permanent magnet generator. The results show that the combination of the proposed EMN method and DWT has very good accuracy and speed. Furthermore, the proposed fault detection method remains unaffected by various linear loads with different power factors.The cover image is based on the article Turn‐to‐turn and phase‐to‐phase short circuit fault detection of wind turbine permanent magnet generator based on equivalent magnetic network modelling by wavelet transform approach by Mehrage Ghods et al., https://doi.org/10.1049/elp2.12452

Detection of Inter-Turn Short Circuits in Induction Motors Using the Current Space Vector and Machine Learning Classifiers

Electric motors play a fundamental role in various industries, and their relevance is strengthened in the context of the energy transition. Having efficient tools and techniques to detect and diagnose faults in electrical machines is crucial, as is providing early alerts to facilitate prompt decision-making. This study proposes indicators based on the magnitude of the space vector stator current for detecting and diagnosing incipient inter-turn short circuits (ITSCs) in induction motors (IMs). The effectiveness of these indicators was evaluated using four machine learning methods previously documented in the literature: random forests (RFs), support vector machines (SVMs), the k-nearest neighbor (kNN), and feedforward and recurrent neural networks (FNNs and RNNs). This assessment was conducted using experimental data. The results were compared with indicators based on discrete wavelet transform (DWT), demonstrating the viability of the proposed approach, which opens up a way of detecting incipient ITSCs in three-phase IMs. Furthermore, utilizing features derived from the magnitude of the spatial vector led to the successful identification of the phase affected by the fault.

Network-based System of Mechanical Vibration Fault Diagnosis

Nowadays researchers are investing in electrical machines fault diagnosis area. The users and manufacturers are strong for containing diagnostic features for reliability and scalability improvement. The regular monitoring enables machine faults early detection and hence helpful for automation by providing process control. The fault detection performance and machine-learning algorithms classification are highly dependent on features involved. The aim of this paper is to solve the network mechanical vibration problem and for that a research on mechanical vibration fault diagnosis is proposed. The first is to use the vibration signal receiving device to record the vibration signal of the target device. In the process of receiving the signal, the measurement point is related to the accuracy of the received signal, so it is necessary to prepare the measurement point. Secondly, the principle of fault detection based on vibration detection is introduced. The main purpose of this method is to identify the fault characteristics, simulate the fault with MATLAB, and obtain the error time-frequency diagram behavior. The feature vector dimension obtained by the idling confirmation example is the same as that of the rotor, which is 14, including 8 relative wavelet packet intensity entropy feature indexes and higher values, minimum value, peak-to-peak value, mean value, mean square error and variance. Finally, the deficiencies of the detected vibration faults are identified and similar improvements are proposed. Improvements only reduce signal vibration, disrupting feature isolation and identifying patterns.The observed percentage accuracy for classification of faults through proposed approach is 98.2%.

A Search Coil Design Method of PMSM for Detection of Inter-Turn Short-Circuit Fault

Fault diagnosis systems based on search coils are widely used for detecting the inter-turn short-circuit (ITSC) fault, which is one of the most common electrical faults in electrical machines. Although various search coil structures have been proposed, there is not a systematic method proposed for the design of search coils. In addition, there is not a quantitative evaluation standard to evaluate the performances of search coils in each aspect, making it difficult to judge whether the proposed search coil structure is the optimal one. In this paper, the reliability, sensitivity and positioning ability of search coils are derived and evaluated by four defined characteristic indexes. And based on these four characteristic indexes, a systematic search coil design method based on the mutual inductance matrix is also proposed. With the proposed design method, the optimal search coil structures for different permanent magnet synchronous machines (PMSMs) are designed and optimized. It is found that the search coils designed by the proposed method have good performance in reliability, sensitivity and positioning ability. The performances of designed search coils are validated by the finite element analysis (FEA) and experiment.

One-Dimensional LSTM-Regulated Deep Residual Network for Data-Driven Fault Detection in Electric Machines

Achieving a model which accurately diagnoses faults in electric machines is a vital step in data-driven fault detection approaches. To this aim, this paper proposes a Long Short-Term Memory (LSTM) regulated deep residual network for data-driven fault diagnosis purposes in electric machines. The advantages of the proposed network are that it is more general in terms of fault type and measurement, results in a more accurate model for fault classification, and has faster convergence compared with other networks, such as conventional deep residual networks. In order to prove these advantages of the proposed network, it is evaluated by two different types of datasets. One is the Inter-Turn Short Circuit (ITSC) fault in a Permanent Magnet Synchronous Motor (PMSM) with the data of measured three-phase current. The second one is the Case Western Reverse University (CWRU) bearing fault dataset with the vibration measurements. The performance of the network is also compared with other networks. Results reveal that the model can accurately detect both types of faults by two different measurements with a test accuracy of 100%. Furthermore, it converges faster than other networks in the training procedure.

Diagnosis of Mechanical and Electrical Faults in Electric Machines Using a Lightweight Frequency-Scaled Convolutional Neural Network

Diagnosis of Mechanical and Electrical Faults in Electric Machines Using a Lightweight Frequency-Scaled Convolutional Neural Network

Simulating Rolling Element Bearing Defects in Induction Machines

Abstract The significant occurrence of bearing faults in electrical machines necessitates continuous online monitoring of the machine’s operating data with the main objective of ensuring both high reliability and efficiency and therefore minimising the chance of unwanted breakdowns. This work focuses on the simulation of (defective) bearings, utilising a dedicated model with five degrees of freedom (DOF) (translational motion) in conjunction with an induction motor model. The primary objective is to gain a comprehensive understanding of how faulty bearings influence both the entire bearing itself and the machine, mainly concerning vibration signals and additional frictional torque. Additionally, various shapes of spalls on the raceway(s) are described, analysed and compared. This work is an extended version of the conference paper ‘Simulating Rolling Element Bearing Defects in Induction Machines’, presenting additional information on how to simulate spalls (with different shapes and sizes) on the inner ring of the bearing. Furthermore, the so-obtained vibration signal is examined and a method is proposed aiming to verify the simulation results and to predict the location of the spall (raceway of the inner or outer ring).

Diagnostics of Early Faults in Wind Generator Bearings Using Hjorth Parameters

Machine learning techniques are a widespread approach to monitoring and diagnosing faults in electrical machines. These techniques extract information from collected signals and classify the health conditions of internal components. Among all internal components, bearings present the highest failure rate. Classifiers commonly employ vibration data acquired from electrical machines, which can indicate different levels of bearing failure severity. Given the circumstances, this work proposes a methodology for detecting early bearing failures in wind turbines, applying classifiers that rely on Hjorth parameters. The Hjorth parameters were applied to analyze vibration signals collected from experiments to distinguish states of normal functioning and states of malfunction, hence enabling the classification of distinct conditions. After the labeling stage using Hjorth parameters, classifiers were employed to provide an automatic early fault identification model, with the decision tree, random forest, support vector machine, and k-nearest neighbors methods presenting accuracy levels of over 95%. Notably, the accuracy of the classifiers was maintained even after undergoing a dimensionality reduction process. Therefore, it can be stated that Hjorth parameters provide a feasible alternative for identifying early faults in wind generators through time-series analysis.

Semi-supervised machine learning for motor eccentricity fault diagnosis

Eccentricity is one major indicator of mechanical faults in electric machines and needs to be detected early to avoid machine failures. Data-driven techniques based on machine learning and deep learning algorithms have been proposed in recent years for motor fault detection. However, majority of these methods use supervised learning algorithms and require large, labelled datasets, which can be challenging to obtain. In this paper, we propose a semi-supervised learning method based on a deep generative model using variational auto-encoder for eccentricity fault quantification. Good prediction accuracy can be achieved when only a small subset of training data has labels.

Real-time intelligent system for wind turbine monitoring using fuzzy system

In these last ten years, the use of wind energy has become a strategic alternative in several countries; the increase in reliability of these systems has become a crucial issue. This is explained by the economic, human or environmental) losses that could generate even if only for a moment, their malfunctioning or their shutdown. In this context, many research methods have been developed to monitor these machines during their operation time. But the problem with these methods was that they did not take into account the criticality, the type of fault, or the degradation degree of the components to trigger an alert, so that the real-time monitoring and the decisions taken were distorted. In this work, four types of defects encountered in wind turbines are studied. Three models of the fuzzy logic system are compared on the severity of the faults in order to know which one is the most efficient. However, an estimation of the fault parameters by the Fast-Estimation of Signal Parameters via Rotational Invariant Techniques (Fast-ESPRIT) algorithm followed by an identification of each fault type by the Classification Algorithm of Fault Harmonics (CAFH) algorithm are first performed. The obtained results show the possibility of monitoring the severity of faults in electric induction machines using the Tsukamoto model in real time. The simulations are performed by using MATLAB software and the obtained results demonstrate the feasibility of such a system.

Recent Trends in Magnetic Sensors and Flux-Based Condition Monitoring of Electromagnetic Devices

As one of the most promising techniques, magnetic flux-based condition monitoring of electrical machines, has gained significant traction in the past decade. One of the key enabling factor is the advancements in sensor technologies to measure both the airgap magnetic flux and stray magnetic flux around the motors with high accuracy. This study presents a comprehensive review of advances in flux sensors and the recent trends in magnetic flux-based diagnostic techniques for various faults and electrical machines. In this context, various magnetic sensor technologies suitable for measuring both the airgap flux and the stray magnetic flux are introduced. Each of the sensor technologies are discussed exhaustively, including their working principles and applications in electrical machine fault diagnostics. In the following sections, various flux-based diagnostic techniques are discussed for different types of common faults in various ac machines. Finally, the future trends are discussed briefly under the light of several applications and their impact.

Online Diagnostic Method for the Detection of High-Resistance Connections and Open-Phase Faults in Six-Phase PMSM Drives

Even though multiphase machines have inherent fault-tolerant capabilities due to their additional degrees of freedom, fault diagnosis still plays an important role to continuously monitor these machines and, thus, prevent critical failures. High-resistance connection (HRC) is one of the most common electric faults in electrical machines and is caused by damaged power connections. These faults create a current unbalance, increasing both the machine losses and torque pulsations, and can eventually lead to open-phase faults (OPFs). This article proposes an online fault diagnostic method to detect HRCs and OPFs in six-phase permanent magnet machine drives. The proposed method is intended for application in electric drives controlled by a predictive current control strategy and does not require the installation of extra hardware. Two variants of the fault diagnostic algorithm are presented in this work: one based on the analysis of the secondary components of the reference current errors and the other variant based on the prediction current errors. Experimental results demonstrate the effectiveness and sensitivity of the proposed diagnostic method for the detection of these types of fault.

A neural network approach to detect winding faults in electrical machine

Abstract In this paper, Neural Network (NN) approach is developed and utilised to detect winding faults in an electrical machine using the samples data of electrical machine in both the healthy and different fault conditions (i.e. shorted-turn fault, phase-to-ground fault and coil-to-coil fault). This is done by interfacing a data acquisition device connected to the machine with a computer in the laboratory. Thereafter, a two-layer feed-forward network with Levenberg–Marquardt back-propagation algorithm is created with the collected input dataset. The NN model developed was tested with both the healthy and the four different fault conditions of the electrical machine. The results from the NN approach was also compared with other results obtained by determining the fault index (FI) of an electrical machine using signal processing approach. The results show that the NN approach can identify each of the electrical machine condition with high accuracy. The percentage accuracy for healthy (normal), shorted-turn, phase-to-ground and coil-to-coil fault conditions are 99, 99.6, 100 and 100% respectively.

Fault Detection based on MCSA for a 400Hz Asynchronous Motor for Airborne Applications

Future health monitoring concepts in different fields of engineering require reliable fault detection to avoid unscheduled machine downtime. Diagnosis of electrical induction machines for industrial applications is widely discussed in literature. In aviation industry, this topic is still only rarely discussed. A common approach to health monitoring for electrical induction machines is to use Motor Current Signature Analysis (MCSA) based on a Fast Fourier Transform (FFT). Research results on this topic are available for comparatively large motors, where the power supply is typically based on 50Hz alternating current, which is the general power supply frequency for industrial applications.
 In this paper, transferability to airborne applications, where the power supply is 400Hz, is assessed. Three phase asynchronous motors are used to analyse detectability of different motor faults. The possibility to transfer fault detection results from 50Hz to 400Hz induction machines is the main question answered in this research work. 400Hz power supply frequency requires adjusted motor design, causing increased motor speed compared to 50Hz supply frequency. The motor used for experiments in this work is a 800W motor with 200V phase to phase power supply, powering an avionic fan. The fault cases to be examined are a bearing fault, a rotor unbalance, a stator winding fault, a broken rotor bar and a static air gap eccentricity. These are the most common faults in electrical induction machines which can cause machine downtime. The focus of the research work is the feasibility of the application of MCSA for small scale, high speed motor design, using the Fourier spectra of the current signal.
 Detectability is given for all but the bearing fault, although rotor unbalance can only be detected in case of severe damage level. Results obtained in the experiments are interpreted withrespect to the motor design. Physical interpretation are given in case the results differ from those found in literature for 50Hz electrical machines.

Assessment of Possessed Generic Green Skills for Green Jobs of Electrical Installation and Maintenance Work Graduates of Technical Colleges in Nigeria

The purpose of this study is to uncover employers’ assessment of possessed generic green skills for green jobs of electrical installation and maintenance work graduates of technical college in Nigeria. The study used 120 out of 140 employers of technical College Graduates in Adamawa State. The instrument used for the data collection was a questionnaire. Mean and standard deviation were used to analyze research data. The hypothesis was tested at 0.05 level of significance. The findings reveal that, in the rewinding of electrical machines modules of electrical installation and maintenance work, technical college graduates have adequate skills to work in the industry except in few areas like: conduct of preventive maintenance and testing faults in electrical filing machines’ winding, the ability to locate faults in electrical filing machines’ winding, carrying out insulation resistance test, interpreting drawings of electrical equipment where they showed averagely adequate and slightly adequate skills. Similarly, from the data obtained and analyzed, the result indicates that the graduates have adequate skills in industrial installation except in few areas like: Ability to handle power transmission equipment and components, maintain simple power tools, to test simple power tools, to interpret drawings of electrical equipment, to read symbols. Therefore, the researchers concluded that technical college graduates have adequate technical skills for green jobs and to work in the industries.

Faults detection and diagnoses of permanent magnet synchronous motor based on neuro-fuzzy network

<p>Faults in electrical machine are very important in order to improve the machine expensive maintenance, efficiency, life time, and reliability at real time, therefore this study deals with Simulink model response for healthy and neuro-fuzzy network (ANFIS), this intelligent technique consist of two parts, the first part include electrical and demagnetization faults while another part deals with mechanical faults. Simulation results obtain record activation, high performance, and efficiency of this network.</p>

Faults detection and diagnoses of permanent magnet synchronous motor based on neuro-fuzzy network

Faults in electrical machine are very important in order to improve the machine expensive maintenance, efficiency, life time, and reliability at real time, therefore this study deals with Simulink model response for healthy and neuro-fuzzy network (ANFIS), this intelligent technique consist of two parts, the first part include electrical and demagnetization faults while another part deals with mechanical faults. Simulation results obtain record activation, high performance, and efficiency of this network.

Detection of the Stator Winding Inter-Turn Faults in Asynchronous and Synchronous Machines Through the Correlation Between Harmonics of the Voltage of Two Magnetic Flux Sensors

This paper presents a statistical methodology for detection of inter-turn short-circuit fault in asynchronous and synchronous machines. This methodology uses a correlation coefficient obtained from external magnetic field measured in the machine vicinity. It is a noninvasive method which follows up the signals of two external flux sensors located symmetrically around the machine axis (180° spatially shifted). The principle is based on the calculation of the Pearson correlation coefficient between two signals delivered by two sensors S1 and S2 when the machine operates at different load conditions, which allows us to detect incipient faults in electrical induction and synchronous machines with a high probability of detection. Experimental tests are realized using two specific rewound machines to create inter-turn short-circuit faults with different severity levels.

Stator Winding Fault Thermal Signature Monitoring and Analysis by In Situ FBG Sensors

Winding short circuit faults are recognized as one of most frequent electric machine failure modes. Effective on-line diagnosis of these is vital, but remains a challenging task, in particular, at incipient fault stage. This paper reports a novel technique for on-line detection of incipient stator short circuit faults in random wound electrical machines based on in situ monitoring of windings thermal signature using electrically nonconductive and electromagnetic interference immune fiber-Bragg grating (FBG) temperature sensors. The presented method employs distributed thermal monitoring, based on the FBG multiplexing feature, in a variety of points within windings, in proximity to thermal hot spots of interest that arise from fault. The ability of the proposed method to enable fault diagnosis through identification of fault-induced localized thermal excitation is validated in steady-state and transient operating conditions on a purpose built inverter driven induction machine test facility. The results demonstrate the capability of unambiguous detection of inter-turn faults, including a single shorted turn. Furthermore, the winding thermal and electrical characteristics at the onset of inter-turn fault are examined and correlated, enabling better understanding of fault diagnostic requirements.