Demagnetization Fault Research Articles

Mechanism-Based Fault Diagnosis Deep Learning Method for Permanent Magnet Synchronous Motor.

As an important driving device, the permanent magnet synchronous motor (PMSM) plays a critical role in modern industrial fields. Given the harsh working environment, research into accurate PMSM fault diagnosis methods is of practical significance. Time-frequency analysis captures the rich features of PMSM operating conditions, and convolutional neural networks (CNNs) offer excellent feature extraction capabilities. This study proposes an intelligent fault diagnosis method based on continuous wavelet transform (CWT) and CNNs. Initially, a mechanism analysis is conducted on the inter-turn short-circuit and demagnetization faults of PMSMs, identifying and displaying the key feature frequency range in a time-frequency format. Subsequently, a CNN model is developed to extract and classify these time-frequency images. The feature extraction and diagnosis results are visualized with t-distributed stochastic neighbor embedding (t-SNE). The results demonstrate that our method achieves an accuracy rate of over 98.6% for inter-turn short-circuit and demagnetization faults in PMSMs of various severities.

Permanent magnet synchronous motor demagnetization fault diagnosis based on PCA-ISSA-PNN

Aiming at the demagnetization fault problem of the permanent magnet synchronous motor (PMSM), a demagnetization fault diagnosis method based on the combination of the principal component analysis (PCA) algorithm, the improved sparrow search algorithm (ISSA), and the probabilistic neural network (PNN) algorithm is proposed. First, the principal components of phase currents are extracted using PCA. Second, ISSA is used to optimize the smoothing coefficients of the PNN algorithm, and the optimized PNN algorithm is combined with PCA to obtain the PCA-ISSA-PNN fault diagnosis model. Finally, the established fault diagnosis model was tested using the current data collected from the experiments and compared with the fault diagnosis indexes and optimization performance of the conventional PNN, PCA-PNN, PCA-GA (genetic algorithm)-PNN, PCA-DA (dragonfly algorithm)-PNN, PCA-GTO (artificial gorilla troop optimizer)-PNN, PCA-AHA-PNN, and PCA-SSA-PNN. The test results show that the fault diagnosis accuracy of PCA-ISSA-PNN reaches 95.83%, and the fault diagnosis indexes are significantly higher than those of PNN, PCA-PNN, PCA-GA-PNN, and PCA-DA-PNN; its optimization performance is also significantly better than that of PCA-GTO-PNN, PCA-AHA-PNN, and PCA-SSA-PNN, which verifies the accuracy and efficiency of the proposed method.

Detection of Demagnetization Faults in Electric Motors by Analyzing Inverter Based Current Data Using Machine Learning Techniques

Demagnetization of the rotor magnets is a significant failure mode that can occur in permanent magnet synchronous machines (PMSMs). Early detection of demagnetization faults can help change system parameters to reduce power output or ensure safety. In this paper, the effects of demagnetization faults were analyzed both in simulation and experiments using the example of drone motors. An approach was investigated to detect even minor demagnetization faults that does not require any additional sensing effort. Machine learning (ML) techniques are used to analyze the phase current data directly received from the inverter to enable anomaly detection. For this purpose, the phase current is transformed by the Fast Fourier Transform (FFT), the spectral data is then reduced in dimensionality, followed by an anomaly detection algorithm using a one-class support vector machine (OC-SVM). To ensure simplified initialization of the ML model without the need for training sets of damaged drives, only data from magnetically undamaged motors was used to train the models for anomaly detection. Different selections of considered harmonics and different metrics were investigated using the experimental data, achieving a precision of up to 99%, a specificity of up to 98%, and an accuracy of up to 90%.

Sliding mode observer-based fault diagnosis and continuous control set fault tolerant control for PMSM with demagnetization fault

A fault detection, estimation and fault tolerant control scheme for demagnetization fault in the permanent magnet synchronous motor systems is proposed in this paper. When demagnetization fault occurs, the system will deviate from the normal state severely. To facilitate the calculation, the demagnetization fault is converted to the dq coordinate system for processing and the resistance change is modeled as the system uncertainty, and the effect of the uncertainty on the system is analyzed. Then a fault detection observer is designed to detect the fault occurrence time, and a sliding mode observer based fault estimation algorithm is designed to estimate the fault and disturbance information. The observer parameters are determined by linear matrix inequality. When the fault information is obtained, a continuous control set fault tolerant control scheme is presented to minimize the impact of fault as far as possible. Finally, experimental results provide evidence of the feasibility of the suggested algorithm.

Diagnostic Method for Demagnetization Fault of Elevator Synchronous Traction Machine based on Informer

Diagnostic Method for Demagnetization Fault of Elevator Synchronous Traction Machine based on Informer

Enhancing Electric Vehicle Reliability: Advanced Fault Detection for Demagnetization Faults in PMSM Motor Using Artificial Neural Network

Enhancing Electric Vehicle Reliability: Advanced Fault Detection for Demagnetization Faults in PMSM Motor Using Artificial Neural Network

Permanent magnet synchronous motor demagnetization fault diagnosis based on leakage radial magnetic density

A method of demagnetization fault diagnosis of permanent magnet synchronous motors is proposed by measuring the radial magnetic density of the leakage field at the external space point of the motor. The method enables non-invasive diagnosis of motor permanent magnet demagnetization faults compared to other methods. In this paper, an analytical model of the radial magnetic density of the leakage field of a surface-mounted permanent magnet synchronous motor under no-load conditions is proposed, and its accuracy is verified by simulation. The influence mode of demagnetization fault on the radial magnetic density of the leakage field is analyzed, which provides a new idea for the diagnosis of motor demagnetization fault.

STUDY OF PERMANENT MAGNET SYNCHRONOUS MOTOR DEMAGNETIZATION FAULT DETECTION USING SIGNAL ANALYSIS TECHNIQUES

Due to its obvious advantages, there is no doubt that Electric vehicles are gaining huge popularity. Thats due to the advantages of electric vehicles when compared to the traditional ICE vehicles. On the other hand, and as electric vehicles are still machines built by human beings, the EVs have several components that can be faulty and affect the vehicle performance and maybe in some stages lead to severe failures or life threatening. These components are mainly the battery, electric motor and controllers. So, many studies focused on studying the electric vehicles components fault detection in the early stages. In this paper, we will focus on one of the most common motor types, the permanent magnet synchronous motors. One of the common PMSM faults related to the rotor is the demagnetization. This fault will be extensively studied using two of the signal analysis method techniques: the output torque and the stator current signal analysis. Using the MATLAB SimuLink, a complete model of the PMSM will be built and the uniform demagnetization fault will be introduced. Then, a comparison between both torque and current signatures will be presented to put our hands on the ability of these techniques to identify the uniform demagnetization fault.

Research on Demagnetization Fault Diagnosis Method of Mine Cutting Permanent Magnet Synchronous Motor

To give timely and accurate diagnosis in the early stage of demagnetization failure for effective control and treatment, based on wavelet packet analysis, principal component analysis (PCA) dimensionality reduction, and least squares support vector machine(LSSVM), the extraction of features and the classification of demagnetization faults are completed. Since it is difficult to collect real data sets of demagnetization faults in practice, a two-dimensional finite element simulation model of permanent magnet synchronous motor (PMSM) under uniform demagnetization and partial demagnetization faults is established based on the Maxwell simulation platform. The wavelet packet analysis is used to extract the demagnetization feature of the A-phase current of the PMSM. Based on PCA dimensionality reduction, the dimensionality reduction of fault features is realized. The LSSVM is used to identify the fault and complete the fault classification. The simulation results show that the method has a high classification accuracy rate for demagnetization faults.

Inter-turn short circuit and demagnetization fault diagnosis of ship PMSM based on multiscale residual dilated CNN and BiLSTM

Inter-turn short circuit (ITSC) and demagnetization of permanent magnet synchronous motors (PMSMs) can lead to serious ship accidents, timely and accurate fault diagnosis of these faults is very important. A multi-signal fusion fault diagnosis method (MD-CNN-BiLSTM) is proposed based on multi-scale residual dilated convolutional neural network (D-CNN) and bidirectional long and short-term memory (BiLSTM) for PMSM fault diagnosis. This method first takes three-phase current and vibration signals as input; uses a three-column parallel CNN structure with different scales to extract both global signal and local feature. A residual connection in the expanded CNN is then used to eliminate the problems of gradient disappearance or explosion; and finally, BiLSTM is used to further extract features and identify the fault. A 2.2 kW permanent magnet synchronous motor was used to build a fault simulation test rig. The motor stator was rewound to simulate the ITSC fault, and different sizes of permanent magnets were replaced to simulate demagnetization fault. ITSC, demagnetization and their coupled faults were simulated under 10 specific motor speeds and loads respectively. The test proved that the diagnostic accuracy of the proposed method was 4.2% higher than that of ordinary CNN and 29.06% higher than that of BiLSTM. It also had the best diagnostic effect under the noise interference of different intensities. It was verified that the proposed method has good noise interference and strong classification ability.

Fault Diagnosis for Permanent Magnet Synchronous Motor With Demagnetization Fault and Sensor Fault

Fault Diagnosis for Permanent Magnet Synchronous Motor With Demagnetization Fault and Sensor Fault

Utilization of Multiple Planar Search Coils for Diagnosing Imbalance Irreversible Demagnetization Faults in PMSMs along the z-Axis

Utilization of Multiple Planar Search Coils for Diagnosing Imbalance Irreversible Demagnetization Faults in PMSMs along the z-Axis

Demagnetization effects of surface-mounted permanent magnet synchronous wind generator

To prevent fossil resources from being depleted and protect the natural balance, renewable resources come to the forefront as an alternative to fossil resources. Wind energy resources, among the renewable energy resources, are important in terms of ensuring the reliability of energy and the use of their resources. Generators are the most important components for the conversion of wind power. Permanent magnet synchronous generators (PMSGs) are preferred in wind turbines since they have high efficiency and high volume/torque densities, thereby optimization of the PMSGs is an important topic for the wind energy community. On the one hand, these machines can cause problems due to overheating and mechanical friction during their long-time operation. To identify the performance lack of the machines due to the de-magnetization faults, systematic work has been performed. When a magnet of a PMSG is de-magnetized at different rates (i.e. 33%, 50%, and 100%), we have explored the artifacts in the electric generation. Besides, the torque performances of the generator at rated load are examined and the flux density distributions are revealed. The rated torque decreased substantially when the demagnetization rate of the magnet increased.

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.

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.

Active Fault-tolerant Control for Surface Permanent Magnet Synchronous Motor Under Demagnetization Fault

This paper introduces a novel method for controlling a surface permanent magnet synchronous motor (SPMSM) during demagnetization fault conditions. The proposed fault-tolerant control (FTC) system incorporates a combination of a fuzzy extended state observer (FESO) based on an interval type 2 fuzzy logic controller (IT2FLC) and second-order sliding mode control (SOSMC) utilizing the super-twisting algorithm. The FESO aims to identify and eliminate demagnetization faults through reconstruction control. The FTC system enhances the dynamic performance and disturbance rejection of the SPMSM, providing a robust solution in the event of a demagnetization fault.

An Improved Method of Permanent-magnet Flux Observer for Interior Permanent Magnet Synchronous Motor Based on Adaptive Theory

Permanent magnet synchronous motor is often used as drive motors for electric vehicles. It is prone to demagnetization failure, and the change of permanent magnet flux link is an important basis for diagnosing demagnetization failure of the motor. First, the study establishes a permanent magnet flux adaptive observer and ensures its stability. Then the sensitivity analysis of the main parameters in the observer is carried out. For the parameter with high sensitivity and a large range of variation during motor operation, the expression of flux observation error caused by this parameter change is derived, and the gain matrix in the observation model is optimized. Through simulation verification, the observer can be used for the demagnetization fault diagnosis of the motor.

Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion.

Permanent magnet synchronous motors (PMSMs) are extensively utilized in production and manufacturing fields due to their wide speed range, high output torque, fast speed response, small size and light weight. PMSMs are susceptible to inter-turn short circuit faults, demagnetization faults, bearing faults, and other faults arising from irregular vibrations and frequent start-brake cycles. While fault diagnosis for PMSMs offers an effective means to enhance operational efficiency, the multi-sensor information fusion is often overlooked. In industrial production processes, the collected data inevitably suffers from noise contamination, which can adversely impact diagnostic outcomes. To enhance the robustness of diagnostic methods in noisy environments and mitigate the risk of overfitting, a PMSM fault diagnosis method based on image features of multi-sensor fusion is proposed. Firstly, the vibration acceleration signals of the PMSM at different positions were acquired. Then, the newly designed multi-signal Gramian Angular Difference Fields (MGADF) method combines sensor signals from three different installation locations into a single image. Next, the multi-texture features are fused to extract the features of the image. Various machine models are compared in the fault feature learning and classification, and the results show that the proposed diagnostic method has good diagnostic accuracy and robustness, with an average diagnostic accuracy of 99.54% and a standard deviation of accuracy of 0.19. It has excellent performance even in noisy environments. The method is non-invasive and can be extended and applied to the condition monitoring and diagnosis of industrial motors.

Demagnetization Fault Diagnosis of Permanent Magnet Direct Drive Generator Based on Improved Residual Neural Network

The permanent magnet direct drive wind turbine is the core equipment of the wind turbine. Barrier analysis ensures its safe and reliable operation. The demagnetization failure of the permanent magnet motor will occur because the wind energy equipment works under bad conditions. Taking the permanent magnet direct drive wind turbine as the research object, this paper uses ANSYS simulation software to build a two-dimensional electromagnetic field model of the generator to simulate the uniform demagnetization fault of the permanent magnet direct drive generator to obtain the back electromotive force waveform under different operating conditions and convert the back electromotive force waveform into images as input data for fault diagnosis of the improved residual neural network. Two kinds of materials are used to verify the effect of the diagnosis model, and the simulation results show that both materials can be used for demagnetizing fault diagnosis, which proves the effectiveness of the fault diagnosis strategy.

Effect Analysis of Demagnetization of Permanent Magnet Synchronous Motor for Electric Drilling Tool on Air-gap Flux Density

Electric drilling tools have been developed in the field of drilling because of their high efficiency. As the core component of electric drilling tool, permanent magnet synchronous motor(PMSM) provides power for drilling tool. However, in the process of drilling, the permanent magnet of PMSM will appear demagnetization.The value of air-gap flux density affects the performance and efficiency of the motor directly. Demagnetization faults are divided into uniform demagnetization and local demagnetization.In order to study the influence of demagnetization of PMSM on the air-gap flux density, mechanism of permanent magnet is analyzed Firstly, and then establishing numerical model with analytical method. Finally, simulation motor demagnetization using finite element method(FEM) based on Maxwell. No-load and load operating state are analyzed under the two fault modes are considered in this paper, and compared with the healthy state of the motor. The analytical results show that when the demagnetization fault occurs in the motor permanent magnet, the air-gap flux density will decrease accordingly.The FEM method results show that with the increase of uniform demagnetization rate, the flux density of the air-gap almost decreases in proportion to the increase of demagnetization rate. The demagnetization of single pole permanent magnet has little effect on the magnetic density of the nearby permanent magnet, but it will lead to the imbalance of the whole electromagnetic field of the motor. The air-gap flux density can be used as a characteristic index to judge the fault of demagnetization.