Detection Of Stator Winding Faults Research Articles

Application of continuous wavelet transform and convolutional neural networks in fault diagnosis of PMSM stator windings

Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. For these reasons, in recent years, more and more research has been focused on developing new methods for fault diagnosis of permanent magnet synchronous motors (PMSMs). This paper proposes a novel hybrid method for the automatic detection and classification of PMSM stator winding faults based on combining the continuous wavelet transform (CWT) analysis of the negative sequence component of the stator phase currents with a convolutional neural network (CNN). CWT scalogram images are used as the inputs of the CNN-based interturn short circuits fault classifier model. Experimental tests were carried out to verify the effectiveness of the proposed approach under various motor operating conditions and at an incipient stage of fault propagation. In addition, the effects of the input image format, CNN structure, and training process parameters on model accuracy and classification effectiveness were investigated. The results of the experimental tests confirmed the high effectiveness of fault detection (99.4%) and classification (97.5%), as well as other important advantages of the developed method.

Stator winding fault detection of induction motors using fast Fourier transform on rotor slot harmonics and least square analysis of the Park's vectors

AbstractAn efficient method for the detection of the stator winding faults (SWFs) of induction motors was dealt based on Park's vector of rotor slot harmonics (RSHs). The induced harmonic voltages of the RSHs in the stator windings are the supreme signatures for fault detection, which can be monitored in the motor currents. Using such a signature, unbalanced supply voltage and stator windings fault situations could be segregated straightforwardly. On the other hand, despite some merits, Park's vector analysis‐based methods suffer from low sensitivity and unreliability in the case of low‐severity faults. To have the advantages of both methods while tackling the shortcomings, Park's vector approach and the RSH signatures are combined in the proposed method. The RSHs are extracted using the fast Fourier transform method and the least squared approach is employed for pattern recognition. Different experimental results under various scenarios, even in the case of low severity faults, confirm that the locus of Park's vector is an elliptical shape in SWFs, while it is shapeless in other cases, such as healthy and unbalanced supply voltage cases.

PMSM Stator Winding Fault Detection and Classification Based on Bispectrum Analysis and Convolutional Neural Network

The diagnosis of Permanent Magnet Synchronous Motor (PMSM) faults has been the subject of much research in recent years, due to the growing reliability and safety requirements for drive systems. This paper concerns PMSM stator winding fault detection and classification. A novel intelligent diagnosis approach is proposed, based on the bispectrum analysis of a stator phase current and the convolutional neural network (CNN). Rather than using raw phase current signals, bispectrum is applied for symptom extraction and utilized as the input for a pretrained CNN model. The CNN model is used for automatic inference on the winding condition of the PMSM stator. Experimental results are presented to validate the proposed algorithm. The classification effectiveness of the developed CNN is as high as 99.4%. This paper also presents the possibility of improving the accuracy of the CNN model and reducing the training time by properly tuning the training parameters. The CNN model learning time is only about one minute. The fault classifier model is developed in Python programming language, avoiding the cost of purchasing additional software.

Fault Diagnosis of PMSM Stator Winding Based on Continuous Wavelet Transform Analysis of Stator Phase Current Signal and Selected Artificial Intelligence Techniques

High efficiency, high reliability and excellent dynamic performance have been key aspects considered in recent years when selecting motors for modern drive systems. These features characterize permanent magnet synchronous motors (PMSMs). This paper presents the application of continuous wavelet transform (CWT) and artificial intelligence (AI) techniques to the detection and classification of PMSM stator winding faults. The complex generalized Morse wavelet used for CWT analysis of three different diagnostic signals—the stator phase current, its envelope and the space vector module—is used to extract the symptoms most sensitive to the interturn short circuits (ITSCs) at the incipient stage of the damage. The effectiveness of automatic stator winding fault classification is compared for three selected ML algorithms: multilayer perceptron, support vector machine and k-nearest neighbors. The effect of the ML models’ hyperparameters on their accuracy is also verified. The high effectiveness of the proposed methodology is confirmed by the results of the experimental verification carried out for different load torque levels and supply voltage frequency values.

Machine Learning-Based Stator Current Data-Driven PMSM Stator Winding Fault Diagnosis

Permanent magnet synchronous motors (PMSMs) have become one of the most important components of modern drive systems. Therefore, fault diagnosis and condition monitoring of these machines have been the subject of many studies in recent years. This article presents an intelligent stator current-data driven PMSM stator winding fault detection and classification method. Short-time Fourier transform is applied in the process of fault feature extraction from the stator phase current symmetrical components signal. Automation of the fault detection and classification process is carried out with the use of three selected machine learning algorithms: support vector machine, naïve Bayes classifier and multilayer perceptron. The concept and online verification of the original intelligent fault diagnosis system with the potential of a real industrial deployment are demonstrated. Experimental results are presented to evaluate the effectiveness of the proposed methodology.

Detection of Stator Winding Faults in PMSMs Based on Second Harmonics of Phase Instantaneous Reactive Powers

Reliable stator winding faults diagnosis is important for machine drives. This work presents an inter-turn short-circuit (ITSC) winding faults detection method for permanent magnet synchronous motors (PMSMs), based on the percentage of the second harmonics in phase instantaneous reactive powers (IRPs), defined in the abc frame of reference. In particular, the proposed indicator can locate the phase with ITSC for the PMSM with neutral conductor. The feature of ITSC in phase IRP is analyzed theoretically first, and a simulation setup is established in MATLAB/Simulink using the faulty PMSM model widely used for ITSC detection. From the theoretical analysis and the simulations, the second harmonic in phase IRP is proven to be the characteristic feature for fault detection. Simulations at different operating conditions show that the percentage of the second harmonic to its dc component in phase IRP, which is the proposed indicator in this article, is more reliable than the second harmonic amplitude itself for the ITSC detection and location. Experimental tests are carried out on a 3 kW motor with ITSC to investigate the effectiveness of the proposed indicator under different working conditions.

Stator winding fault detection of an Alternator using MATLAB

The paper represents the comprehensive analysis of stator winding faults of an alternator. An alternator is main component of power system which plays a big role in whole system for security and stability. The conventional system only detect symmetrical and unsymmetrical faults, they will not detect inter turn fault. The fault detection of stator winding is an effective and important measure to ensure the safe operation of generator. Early detection of stator winding fault would eliminate damage to stator core & adjacent coils, also reduce repairing cost and outage time of generator. As stator winding fault causes unbalance in phase voltage, this concept is discussed in this paper for the detection of stator winding fault. The negative sequence voltage of generator is used as fault indicator for fault detection. This new method is done using MATLAB software. Also, this method operate for external as well as internal fault and helps the generator winding keep healthy. Keywords— stator winding fault, internal negative sequence voltage, internal and external faults.

Stator winding fault detection of permanent magnet synchronous motors based on the bispectrum analysis

Stator winding fault detection of permanent magnet synchronous motors based on the bispectrum analysis

Stator Winding Fault Detection of Permanent Magnet Synchronous Motors Based on the Short-Time Fourier Transform

Abstract In modern drive systems, the high-efficient permanent magnet synchronous motors (PMSMs) have become one of the most substantial components. Nevertheless, such machines are exposed to various types of faults. Hence, on-line condition monitoring and fault diagnosis of PMSMs have become necessary. One of the most common PMSM faults is the stator winding fault. Due to the destructive character of this failure, it is necessary to use fault diagnostic methods that allow fault detection at its early stage. The article presents the results of experimental studies obtained from fast Fourier transform (FFT) and short-time Fourier transform (STFT) analyses of the stator phase current, stator phase current envelope and stator phase current space vector module. The superiority of the proposed method over the classical approach based on the stator current analysis using FFT is highlighted. The proposed solution is experimentally verified under various motor operating conditions. The application of STFT analysis discussed so far in the literature has been limited to the fault diagnosis of induction motors and the narrow range of the analysed motor operating conditions. Moreover, there are no works in the field of motor diagnostics dealing with STFT analysis for stator windings based on the stator current envelope and the stator current space vector module.

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits.

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Application of simplified convolutional neural networks for initial stator winding fault detection of the PMSM drive using different raw signal data

Permanent magnet synchronous motors (PMSM) have become one of the most substantial components of modern industrial drives. These motors, like all the others, can unfortunately undergo various failures, causing production line downtime and resulting losses. Accordingly, it is necessary to develop fault diagnostic techniques which detect the damages at the earliest possible stage. This study presents a method of detecting incipient faults of the PMSM stator windings using direct signal analysis and a convolutional neural network (CNN). During the tests, the structures of CNN were optimised to constitute a balance between the high efficiency of fault detection and a small number of network parameters. The effectiveness of the CNNs with inputs constituted by different electrical signals measured in the drive system is compared. Three raw data signals are tested as CNN inputs, namely: stator phase currents, phase-to-phase voltages and axial flux, without data preprocessing. The article aims to show the possibility of detecting the incipient interturn short circuits in the PMSM stator winding based on the information obtained directly from the measured signals as well as to present the influence of the drive operating conditions and the type of measurement signals used on the structure and performance of the developed CNNs.

Robust Stator Winding Fault Detection in PMSMs With Respect to Current Controller Bandwidth

Permanent magnet synchronous machines work with controllers and controller parameters affect the machine's voltages and currents and, thus, the diagnosis of stator winding fault. One such parameter is the current controller bandwidth that affects the harmonic content in these signals. In the literature, the second harmonics in both dq-axis currents and voltages have been used for fault detection, and the main advantage is that interturn short-circuit (ITSC) winding fault with a few shorted turns, specially one turn, can be detected. One indicator that combines the fault information in voltages and currents is the instantaneous reactive power (IRP). However, it has been shown that IRP does not perform robustly with respect to current controller bandwidth. The main contribution of this article is that it proposes and validates a new mathematical function for ITSCs detection, Rayleigh quotient, which combines the fault information present in voltages and currents to make ITSCs detection robust to current controller bandwidth. The validations from both cosimulation and experiment show that the proposed method is robust to the bandwidth and can detect ITSC faults faster than IRP.

Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

Induction motors (IMs) are the workhorse of the industry and are subjected to a harsh environment. Due to their operating conditions, they are exposed to different kinds of unavoidable faults that lead to unscheduled downtimes and losses. These faults may be detected early through predictive maintenance (i.e., deployment of condition monitoring systems). Motor current signature analysis (MCSA) is the most widely used technique to detect various faults in industrial motors. The stator winding faults (SWF) are one of the major faults. In this paper, we present an induction motor fault detection and identification system using signal processing techniques such as fast Fourier transform (FFT), short-time Fourier transform (STFT), and continuous wavelet transform (CWT). A three-phase motor model is developed in MATLAB Simulink and simulated under various fault conditions. The current signature is observed using FFT, spectrogram, and scalogram to detect the faults. It is observed that under some fault conditions, the current signature analysis remains indistinguishable from the non-fault case. Therefore, deep learning (DL) methods are adopted here to identify these faults. The time-series data of healthy and unhealthy conditions are obtained from the simulation results. The comparative investigation among DL models confirmed the superiority of the long short-term memory (LSTM) model, which achieved 97.87% accuracy in identifying the individual faults.

Efficiency of Cascaded Neural Networks in Detecting Initial Damage to Induction Motor Electric Windings

This article presents the efficiency of using cascaded neural structures in the process of detecting damage to electrical circuits in a squirrel cage induction motor (IM) supplied from a frequency converter. The authors present the idea of a sequential connection of classic neural structures to increase the efficiency of damage classification and detection presented by individual neural structures, especially in the initial phase of single or multiple electrical failures. The easily measurable axial flux signal is used as a source of diagnostic information. The developed cascaded neural networks are implemented in the measurement and diagnostic software made in the LabVIEW environment. The results of the experimental research on a 1.5 kW IM supplied by an industrial frequency converter confirm the high efficiency of the use of the developed cascaded neural structures in the detection of incipient stator and rotor winding faults, namely inter-turn stator winding short circuits and broken rotor bars, as well as mixed failures in the entire range of changes of the load torque and supply voltage frequency.

Stator winding fault detection of induction generator based wind turbine using ANN

This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.

Automatic Condition Monitoring of Industrial Machines Using FSA-Based Hall-Effect Transducer

A reliable and swift real-time condition monitoring for an industrial machine is a need of the hour when dealing with critical uncontrollable machine failures such as stator winding insulation failure, which is more prominent while working in robust conditions. Early detection of stator winding faults, especially turn-turn fault, occurred mainly as a short-circuit between the winding of the same phase due to the insulation failure, which can prevent fault propagation and protect winding from further insulation damages. This paper presents steady and transient state analysis of three-phase induction motor using fault signature analyzer based hall-effect transducer under various fault conditions. A numerical model with stator winding faults has been implemented, taking into account two major faults such as stator turn-turn (STTF) and stator phase-neutral fault (SPhN), using finite element method (FEM). FEM obtains the symmetrical current components of the motor under different fault conditions using finite element analysis (FEA). The machine torque has also been modeled and compared in a healthy and faulty condition. The numerical finite element method (FEM) model could identify a healthy and faulty induction machine with 95% accuracy using finite element analysis (FEA) and the same was validated using experimental setup results under the same operating conditions.

Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.

Stator Winding Fault Detection Using External Search Coil and Artificial Neural Network

This paper presents a methodology for winding stator fault detection of induction motors, using an external search coil, which is a noninvasive technique and can be applied during motor operation. The dispersion magnetic flux of the motor operating in abnormal conditions induces a voltage in the search coil that differs from a reference pattern corresponding to the healthy stator winding. Experimental data were obtained in a test bench using a 0.75 kW three-phase squirrel-cage induction motor with the stator winding modified to allow the introduction of short circuits. This work considered short circuits in one phase, involving 1%, 3%, 5% and 10% of the turns, with the motor loaded with a varying torque. Fault diagnosis is obtained through two models of artificial neural networks, implemented with the signals in the time domain. The obtained results demonstrated that the developed methodology presents difficulties in predicting short circuits in incipient stages, but for short circuits of higher severity, the behaviour improved substantially, being 100% successful for faults with 10% turns short-circuited.

Stator Fault Detection of Induction Motor Using Walsh-Hadamard Transform

AbstractFault detection in induction motors has drawn growing attention due to importance of these motors in different industries. Analysis of motor currents is a common method for the fault detection. This method is preferred compared with other approaches because of its economic and technical superiorities. In this paper, a modified model of an induction motor including stator fault and a new technique are proposed for detection of stator winding faults. Firstly, Walsh-Hadamard transform is applied to stator currents. Then, variation of Walsh-Hadamard transform is used as the criterion for the detection. Plenty of simulation cases are utilized to evaluate the method. To enhance accuracy of the model, isolation layer resistance(Rf)is also taken into account. For further evaluation, a large number of experimental data were recorded from different experiments to assess the method in practice.