Faults In Permanent Magnet Synchronous Motors Research Articles

An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles

This article provides a comprehensive overview of state-of-the-art techniques for detecting and diagnosing stator winding inter-turn short faults (ITSFs) in permanent-magnet synchronous motors (PMSMs) for electric vehicles (EVs). The review focuses on the following three main categories of diagnostic approaches: motor model-based, signal processing-based, and artificial intelligence (AI)-based fault detection and diagnosis methods. Motor model-based methods utilize motor state estimation and motor parameter estimation as the primary strategies for ITSF diagnosis. Signal processing-based techniques extract fault signatures from motor measured data across time, frequency, or time-frequency domains. In contrast, AI-based methods automatically extract higher-order fault signatures from large volumes of preprocessed data, thereby enhancing the effectiveness of fault diagnosis. The strengths and limitations of each approach are thoroughly examined, providing valuable insights into the advancements in ITSF detection and diagnosis techniques for PMSMs in EV applications. The emphasis is placed on the application of signal processing methods and deep learning techniques in the diagnosis of ITSF in PMSMs in EV applications.

Permanent magnet synchronous motor inter-turn short circuit diagnosis based on physical-data dual model under oil-drilling environment

Inter-turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motor (PMSM) have gained significant attention due to the growing demand for enhanced reliability and safety in actuation systems. This paper presents a adaptive fault diagnosis approach specifically designed for ITSC faults in PMSM used in oil-drilling applications, where sparse actual data and varying speed conditions pose considerable sparse training data and ITSC feature extraction challenges respectively. To address these challenges, we first construct a physical fault model of PMSM-ITSC and establish a simulation experiment platform to replicate downhole environments with high temperatures and rapidly changing PMSM speeds, ensuring a reliable data source for analysis. Subsequently, we propose a novel adaptive peak-to-peak self-finding method (APPS) that leverages frequency-domain prior knowledge to adaptively extract ITSC fault characteristics, even amidst drastic changes in PMSM speed. Furthermore, we introduce the time-sequence efficient moving window self-attention network (EMWSAN) data model for inferring the stator phase winding state, incorporating Half-sandwich and Cascaded windows group attention operations. This approach significantly reduces computational complexity and network parameters compared to traditional self-attention mechanisms. To expedite the fitting process, we apply transfer learning (TL) theory, transferring knowledge from the physical knowledge to the data model, enabling EMWSAN to be trained more efficiently. As a result, our proposed ITSC fault diagnosis scheme achieves an impressive 96.72% classification accuracy, achieving existing state-of-the-art methods.

Adversarial domain adaptation network with MixMatch for incipient fault diagnosis of PMSM under multiple working conditions

The incipient faults diagnosis under multiple working conditions has been a challenging problem due to the small difference of incipient fault features, and different feature divergences and distributions under different working conditions. In this paper, an Adversarial Domain Adaptation Network with MixMatch (ADANM) is proposed for the incipient faults diagnosis of permanent magnet synchronous motor (PMSM) under multiple working conditions. In ADANM, through selecting multi-scale convolutional kernels, an Adaptive Multi-scale Convolutional Neural Network (AM-CNN) is designed to improve the adaptability of extracted features for the incipient faults of PMSM under different working conditions. Meanwhile, the MixMatch and confidence-thresholding embedded in the adversarial network are employed to reduce the conditional distribution discrepancy by reducing the number of false pseudo-labels. The proposed ADANM is evaluated by diagnosing 4 types of incipient faults in PMSM under multiple working conditions. Extensive experiments are conducted on the hardware-in-the-loop (HIL) real-time platform, and our method outperforms existing methods by an average accuracy increase of approximately 8.12% across three different datasets. This significant improvement validates the effectiveness and superiority of the proposed method.

Automated Monitoring of the Uniform Demagnetization Faults in Permanent-Magnet Synchronous Motors: Practical Methods and Challenges

Due to its high power, high efficiency, low pollution, and compact size, permanent-magnet synchronous motors (PMSMs) have been widely used in a variety of fields, including electric vehicles, aerospace, wind turbines, and marine devices, which are used in renewable, sustainable, and environmentally friendly energy resources. However, in these practical scenarios, the motor operating conditions are complex and variable. Under high-temperature and high-current conditions, PMSMs may experience demagnetization failures, not only leading to performance degradation but also inducing unexpected failures of the motors. To reduce the risk of unexpected losses caused by demagnetization faults and improve the safety and reliability of motor systems, it is necessary to apply automated monitoring of the magnet flux of the motor’s permanent magnets and achieve real-time diagnosis of early demagnetization faults, ensuring the safe operation of the motor. This review article tries to summarize the current detection methods of the automated monitoring of demagnetization faults in PMSMs. The main online monitoring technologies from both practical and academic perspectives are summarized and their benefits and challenges are reviewed. Finally, the research trends and suggestions for future improvements are provided. This review article not only sheds light on the origins of the automated monitoring of demagnetization faults but also helps to design highly effective and sustainable permanent-magnet synchronous motors.

On Model-Based Transfer Learning Method for the Detection of Inter-Turn Short Circuit Faults in PMSM.

The early detection of an inter-turn short circuit (ITSC) fault is extremely critical for permanent magnet synchronous motors (PMSMs) because it can lead to catastrophic consequences. In this study, a model-based transfer learning method is developed for ITSC fault detection. The contribution can be summarized as two points. First of all, a Bayesian-optimized residual dilated CNN model was proposed for the pre-training of the method. The dilated convolution is utilized to extend the receptive domain of the model, the residual architecture is employed to surmount the degradation problems, and the Bayesian optimization method is launched to address the hyperparameters tuning issues. Secondly, a transfer learning framework and strategy are presented to settle the new target domain datasets after the pre-training of the proposed model. Furthermore, motor fault experiments are carried out to validate the effectiveness of the proposed method. Comparison with seven other methods indicates the performance and advantage of the proposed method.

Empirical mode decomposition and Hilbert–Huang transform-based eccentricity fault detection and classification with demagnetization in 120 kW interior permanent magnet synchronous motors

Permanent magnet synchronous motors (PMSMs) are extensively used in electric vehicles (EVs) owing to their high power density and efficiency. However, their safety and reliability have not been verified sufficiently. Failure of PMSMs in EVs can cause performance degradation, increased maintenance costs, and catastrophic accidents. Therefore, early detection of faults in PMSMs is necessary for an efficient and safe operation. In this study, static, dynamic, and mixed eccentricity faults in 120 kW interior permanent magnet synchronous motors (IPMSMs) were diagnosed and distinguished from uniform demagnetization fault by monitoring the Hilbert spectrum of the stator current. Experiments were carried out for different speeds, loads, and fault severities to analyze the effect of operating conditions on the proposed fault detection method. The phase current data were decomposed into set of intrinsic mode functions (IMFs) with empirical mode decomposition (EMD) method and Hilbert transform was applied for each IMFs to calculate the instantaneous frequency and energy. The resultant Hilbert spectrum exhibited instantaneous frequency distortions with an instantaneous energy surge under an eccentricity fault. The effect of thresholding in EMD was analyzed to improve the accuracy of the proposed method under high-load and high-speed conditions. Also, the experimental results showed that eccentricity and uniform demagnetization faults can be classified by monitoring the overall instantaneous energy.

PMSM Demagnetization Fault Diagnosis Based on Back-EMF Signal

To study the irreversible demagnetization fault of the permanent magnets (PMs) in the permanent magnet synchronous motor (PMSM), a two-dimensional Finite Element (FE) model of the PMSM is built by the Finite Element Method (FEM). Analysis of demagnetization faulty mechanism of the electric and magnetic signals under local demagnetization conditions is conducted. The no-load Back-EMF signal is adopted as the indicator of the demagnetization faults of PMSM. In this paper, the faults of uniform demagnetization and single magnet demagnetization are studied through FEA. Therefore, the relationship between the amplitude of the back EMF and the degree of demagnetization is obtained. Moreover, the fitting curve is obtained by using the least squares method from multiple sets of FEA results. The results show that the method can effectively diagnose the demagnetization fault of the PMs of the PMSM, and could provide the guideline for online health monitoring of the PMSM in future research works.

Airgap Search Coil Based Identification of PM Synchronous Motor Defects

Online detection of rotor and load faults in permanent magnet synchronous motors (PMSM) based on spectrum analysis of current or vibration is not capable of identifying the root cause of the fault, since all faults produce identical fault signatures. The sensitivity of fault detection also depends on the motor and controller design making fault detection unreliable. In addition, operation under variable frequency and load limits the effectiveness of spectrum analysis based methods. In this article, airgap flux monitoring is investigated as an alternative for providing reliable identification of rotor and load faults in PMSMs. Based on the analysis of airgap flux under partial and uniform demagnetization, dynamic eccentricity, and load unbalance, an airgap search coil voltage based method for detection and classification of the faults is proposed. The claims made in the article are verified through experimental testing on an IPMSM under emulated fault conditions along with a comparison to vibration and current spectra-based detection. It is shown that the proposed method provides reliable online identification of the faults for cases where conventional spectrum analysis based methods fail.

Inter-Turn Short Circuit Fault Diagnosis of PMSM

Permanent Magnet Synchronous Motor (PMSM) is widely used due to its advantages of high power density, high efficiency and so on. In order to ensure the reliability of a PMSM system, it is extremely vital to accurately diagnose the incipient faults. In this paper, a variety of optimization algorithms are utilized to realize the diagnosis of the faulty position and severity of the inter-turn short-circuit (ITSC) fault, which is one of the most destructive and frequent faults in PMSM. Compared with the existing research results gained by particle swarm optimization algorithms, in this paper, the methods using other optimization algorithms incorporating genetic algorithm, whale optimization algorithm and stochastic parallel gradient descent algorithm (SPGD) can acquire more stable and precise results. In particular, the method based on SPGD can obtain the most desirable performance among the methods mentioned above; that is, the relative error of short-circuit turns ratio is approximately as low as 0.03%. In addition, in the case of asymmetric input three-phase voltage and with the adverse impact of high-order harmonics at different load moments, the fault diagnosis method based on SPGD still maintains relatively satisfactory properties. Finally, the verification on the actual PMSM platform demonstrates that the SPGD can still diagnose the faulty severity.

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.

Demagnetization Fault Detection and Location in PMSM Based on Correlation Coefficient of Branch Current Signals

To address such challenges as an uncertain number of demagnetization poles of the permanent magnet synchronous motor (PMSM) and cases in which the fault cannot be located, this paper proposes a fault identification and location methodology based on the analysis of the motor stator current. First, the influence of the irreversible demagnetization of permanent magnets on the analytical model of the back electromotive force (Back-EMF) of the rotor in a single motor stator slot is analyzed. Moreover, considering the topology of the motor, the influence of the demagnetization fault on the stator phase current and branch current is analyzed. Since the stator phase currents cannot diagnose the partial demagnetization faults of PMSM with some topological structures, the stator branch current is selected as the signal for the identification and localization of the demagnetization fault. Secondly, the demagnetization fault diagnosis and mode recognition of the motor are carried out through the amplitude of the real-time branch current and the harmonic components of the PMSM. A sample database of demagnetization faults is established through calculation and normalization of the residual value of the stator branch current and the branch current of the healthy motor after demagnetization in one pole order. The fault threshold is obtained by analyzing the residual of the branch current of uniform demagnetization and the Pearson correlation coefficient of the fault sample database. Then, the correlation coefficient between the real-time branch current residual value of PMSM and the fault sample database is analyzed, and the number of demagnetization poles and the fault location are determined by the number and location of the calculated correlation coefficient exceeding the threshold. Finally, the feasibility and effectiveness of the proposed method are verified by the finite element analysis (FEA) results.

Diagnosis of Interturn Short Circuit of Permanent Magnet Synchronous Motor Based on Stacked Normalized Sparse Autoencoder

Regarding the problems such as the traditional auto-encoders’ tendency to learn the similar features during the feature extraction and limited capability of feature learning in the shallow network models, the paper puts forward an interturn short circuit diagnosis method for Permanent Magnet Synchronous Motor (PMSM) based on the Stacked Normalized Sparse Autoencoder (SNSAE). The method is to guarantee the representativeness of the extracted features, acquire stronger anti-interference capability, and is more suitable for training diversified and large-capacity mechanical equipment failure data. Four types of Inter-Turn Short-Circuit (ITSC) fault data were collected on the PMSM fault diagnosis experimental platform for testing. The experimental outcome suggests that the SNSAE algorithm can effectively extract the fault features, and the fault diagnosis effect is better than the traditional intelligent diagnosis method, and its diagnosis accuracy is as high as 98.91%.

Fault Feature Extraction Method of a Permanent Magnet Synchronous Motor Based on VAE-WGAN

This paper focuses on the difficulties that appear when the number of fault samples collected by a permanent magnet synchronous motor is too low and seriously unbalanced compared with the normal data. In order to effectively extract the fault characteristics of the motor and provide the basis for the subsequent fault mechanism and diagnosis method research, a permanent magnet synchronous motor fault feature extraction method based on variational auto-encoder (VAE) and improved generative adversarial network (GAN) is proposed in this paper. The VAE is used to extract fault features, combined with the GAN to extended data samples, and the two-dimensional features are extracted by means of mean and variance for visual analysis to measure the classification effect of the model on the features. Experimental results show that the method has good classification and generation capabilities to effectively extract the fault features of the motor and its accuracy is as high as 98.26%.

Modeling and Detection of Phase Current Sensor Gain Faults in PMSM Drives

The fault of current sensors in AC electric drives can cause inaccurate tracking of the control reference and torque oscillations, that can lead to damage of mechanical components. Therefore, the development of accurate detection techniques of these faults plays a crucial role for the proper management of the electric drive and for the fault isolation and estimation. In the paper, a model-based method for the detection of phase current sensor gain faults in a Permanent Magnet Synchronous Motor (PMSM) drive with Field Oriented Control (FOC) is proposed. At first, a mathematical model is presented, which allows an accurate determination of the analytical closed-form expression of the steady-state stator currents, taking into account the effects both of the current control regulators and of any current sensors gain faults. Starting from this model, a low-computation algorithm has been carried out, which allows not only to detect and isolate the current sensors affected by the gain fault, but also to estimate the gain values starting from the measured phase currents and motor speed. The model and the diagnostic algorithm performance is verified by means of numerical and experimental results.

Fault Diagnosis of Permanent Magnet Synchronous Motor Inter Turn Short Circuit Based on Deep Reinforcement Learning

With the increasing market share of Permanent Magnet Synchronous Motor(PMSM), the fault diagnosis and prediction technology for PMSM is becoming increasingly important. Firstly, in order to solve the problem of insufficient fault sample data consisting of negative sequence current, electromagnetic torque and other inter turn short circuit fault feature terms, the Conditional Generation Adversarial Network(CGAN) is used to expand the data set. Then, with sufficient data, Dueling_DQN algorithm of deep reinforcement learning is used to train and optimize the extended data set. Finally, the effectiveness of the algorithm in the field of PMSM fault diagnosis is verified by simulation training. The results show that the fault diagnosis accuracy of the algorithm can be reached 97.5%, while improved the convergence speed and saved the time cost of fault diagnosis.

Diagnosis and discernment between eccentricity and demagnetization faults in PMSM drives

This paper proposes a new approach that allows the diagnosis and the ability of discernment between the frequency signatures of eccentricity faults and those of demagnetization in a permanent magnet synchronous motor (PMSM). This approach is based on the application of a very selective band-pass filter in the stator current. A judicious calculation of the parameters of this filter makes it possible to extract only the frequency signatures of searched faults. The use of the relevant information on the magnitude and phase angle of the signatures, obtained by filtering, makes it possible to distinguish between both of the studied faults. The simple monitoring of the plotting in terms of polar coordinates of the combined information (magnitude/phase angle) of the signature’s characteristic of these two faults allows for this discernment. Experimental results show the effectiveness of the proposed approach when it comes to the diagnosis reliability of demagnetization and the eccentricity faults in a PMSM.

A hybrid diagnosis method for inverter open-circuit faults in PMSM drives

In order to improve the evaluation process of inverter open-circuit faults diagnosis in permanent magnet synchronous motor (PMSM) drives, this paper presents a diagnosis method based on current residuals and machine learning models. The machine learning models are introduced to make a comprehensive evaluation for the current residuals obtained from a state observer, instead of evaluating the residuals by comparing with thresholds. Meanwhile, fault diagnosis and location are conducted simultaneously by the machine learning models, which simplifies the diagnosis process. Besides, a sampling strategy is designed to implement the proposed scheme online. Experiments are carried out on a DSP based PMSM drive, and the effectiveness of the proposed method is verified.

Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG

Predictive maintenance in the permanent magnet synchronous motor (PMSM) is of paramount importance due to its usage in electric vehicles and other applications. Recently various deep learning techniques are applied for fault detection and identification (FDI). However, it is very hard to optimally train the deeper networks like convolutional neural network (CNN) on a relatively fewer and non-uniform experimental data of electric machines. This paper presents a deep learning-based FDI for the irreversible-demagnetization fault (IDF) and bearing fault (BF) using a new transfer learning-based pre-trained visual geometry group (VGG). A variant of ImageNet pre-trained VGG network with 16 layers is used for the classification. The vibration and the stator current signals are selected for the feature extraction using the VGG-16 network for reliable detection of faults. A confluence of vibration and current signals-based signal-to-image conversion approach is also introduced for exploiting the benefits of transfer learning. We evaluate the proposed approach on ImageNet pre-trained VGG-16 parameters and training from scratch to show that transfer learning improves the model accuracy. Our proposed method achieves a state-of-the-art accuracy of 96.65% for the classification of faults. Furthermore, we also observed that the combination of vibration and current signals significantly improves the efficiency of FDI techniques.

Open circuit fault detection and FTC for sensorless PMS motor control based on the back-EMF SMO

ABSTRACT The detection of the electrical fault in speed controller under a Motor Control Unit System (MCUS) has been amply viewed in the research paper. Nevertheless, the research work in this domain have been constantly followed on sensor-based vector control of synchronous motor. This paper combines the Power Inverter Voltage (PIV) faults detection (signal-based approach) with sliding mode technique (model-based) to provide Fault Tolerant Control (FTC) for sensorless speed Permanent Magnet Synchronous (PMS) motor drive. Back-EMF sliding mode observer (Back-EMF-SMO) is used for rotor position and stator currents estimation and simultaneously MCUS fault diagnosis. The fault diagnosis for MCUS faults of PMS motor drives using a Hilbert Transform. The motor phase currents signals shapes for various modes of MCUS faults are examined. A PIV Faults Detection and Isolation (FDI) algorithm is used to allow the switchover to a redundant leg. The obtained findings exhibit the effectiveness of the suggested FDI process particularly to detect and to isolate a PIV faults for sensorless PMS motor drives.

A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis.

To solve the problem of low accuracy in traditional fault diagnosis methods, a novel method of combining generalized frequency response function(GFRF) and convolutional neural network(CNN) is proposed. In order to accurately characterize system state information, this paper proposed a variable step size least mean square (VSSLMS) adaptive algorithm to calculate the second-order GFRF spectrum values under normal and fault states; In order to improve the ability of fault feature extraction, a convolution neural network (CNN) with gradient descent learning rate and alternate convolution layer and pooling layer is designed to extract the fault features from GFRF spectrum. In the proposed method, the second-order GFRF spectrum of each state of Permanent Magnet Synchronous Motor (PMSM) is obtained by VSSLMS; Then, the two-dimension GFRF spectrum, which is regarded as the gray value of the image,will be further transformed into image. Finally, the CNN is trained with learning rate by gradient descent way to realize the fault diagnosis of PMSM. Experimental results indicate that the accuracy of proposed method is 98.75%, which verifies the reliability of the proposed method in application of PMSM fault diagnosis.