Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering

Chang, Lien Kai; Wang, Shun Hong; Tsai, Mi Ching

doi:10.3390/en13174467

Cited by 12 publications

(4 citation statements)

References 23 publications

(28 reference statements)

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“…When analyzing the designs of PMSM motors intended for electric vehicles, it is also necessary to focus on possible motor damage and motor durability. The number of publications concerning the diagnostics of electric vehicle drives proves that this topic is still important [39][40][41][42]. Most of those works focused on methods of machine diagnostics in terms of the occurrence of the phenomenon of permanent magnet demagnetization as a result of improper power supply, control, emergency short-circuits, and other unfavorable operating conditions.…”

Section: Study On Winding Short-circuit Faultmentioning

confidence: 99%

Influence of a Winding Short-Circuit Fault on Demagnetization Risk and Local Magnetic Forces in V-Shaped Interior PMSM with Distributed and Concentrated Winding

et al. 2021

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This paper presents a comparison of 30/8 and 12/8 AC permanent magnet motors with distributed (DW) and concentrated winding (CW) designed for electric vehicle traction. Both prototypes are based on an interior permanent magnet (IPM) motor topology and contain V-shape magnets. The radial flux AC IPM motors were designed for an 80 kW propulsion system to achieve 125 N·m. Finite element models (FEM) used to design the geometry of IPM motors and the required useful parameters of electric motors are widely investigated. The accuracy of finite element models is verified and validated on the basis of test data. Numerical simulations of healthy and faulty operation states, and studies of winding faults based on the FEM offer a deeper understanding of the associated phenomena. Therefore, in this paper, a short-circuit fault in a stator winding was simulated to investigate the transient currents under an external load collapse, for all winding phases. These simulations were used to define other important machine parameters to improve mechanical reliability of the motors and to assess the potential risk of permanent magnet (PM) demagnetization. Furthermore, the analysis of local magnetic forces affecting the PMs in the rotor and their possible displacement in a short-circuit situation were performed, also taking into account the centrifugal force. Lastly, it is demonstrated that the choice of winding configuration has a significant impact on the uncontrolled displacement of magnets in the rotor.

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Section: Study On Winding Short-circuit Faultmentioning

confidence: 99%

Influence of a Winding Short-Circuit Fault on Demagnetization Risk and Local Magnetic Forces in V-Shaped Interior PMSM with Distributed and Concentrated Winding

et al. 2021

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“…For example, after processing the data by ensemble empirical mode decomposition (EEMD) and linear discriminant analysis (LDA), Hou et al [ 2 ] used Gath-Geva clustering algorithm (GG) to identify the faults of rolling bearing and got a satisfactory clustering result with better intraclass compactness. Chang et al[ 3 ] achieved 96% accuracy of permanent magnet synchronous motors demagnetization fault diagnosis by auto-encoder and K-means algorithm. In addition, Li et al [ 4 ] integrated K-means in the neural network architecture for unsupervised learning and proposed a deep representation clustering-based diagnosis model to address the data sparsity issue in data-driven machinery fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

An Improved Bearing Fault Diagnosis Model of Variational Mode Decomposition Based on Linked Extension Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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In bearing fault diagnosis, due to the insufficient obtained supervised data and the inevitable noise contained in the vibration signals, the problem of clustering bearing fault diagnosis with imbalanced data containing noise is caused. Thanks to the ability to quickly and fully learn boundary information in small samples, the extension neural network-type 2 algorithm (ENN-2) has the potential in imbalanced data clustering and has been gradually applied in fault diagnosis. Therefore, in order to improve the unstable clustering performance of ENN-2 caused by its heavy dependence on input order of samples, a novel algorithm called linked extension neural network (LENN) is developed by redesigning the correlation function and its iterative method, which greatly reduces the clustering iteration epochs of the algorithm. In addition, an evaluation index of clustering quality for this novel algorithm, extension density, is also proposed. After that, a bearing fault diagnosis model of variational mode decomposition (VMD) based denoising and LENN is proposed. Firstly, VMD is used to get intrinsic mode functions (IMFs), and the correlation coefficients of IMFs are calculated for signal denoising. Secondly, the features are extracted from denoised signals and selected by PCA algorithm, and the fault diagnosis is finally completed by LENN. Compared with ENN-2, K-means, FCM, and DBSCAN based models, the proposed model identifies the faults with different severities more accurately and achieves superior diagnostic ability on different imbalance degrees of datasets, which can further lay a foundation for clustering fault diagnosis based on vibration signals.

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“…With the further improvement of rural roads in China, small agricultural electric passenger and freight vehicles are in high demand. The IPMSM has the characteristics of small size, simple control, large torque, high power density and high efficiency; therefore, it can be used in small agricultural electric passenger and cargo vehicles [1]. Due to the rugged rural roads and as no breakthrough has been made on the lack of battery power density, the requirement of this type of vehicle is to ensure that the vibration is small and the maximum torque and minimum energy consumption are guaranteed at the same time.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Optimization of Permanent Magnet Synchronous Motor Based on Taguchi Method and PSO Algorithm

Zhao

Zeng

et al. 2022

Energies

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To solve the optimization issues of interior permanent magnet synchronous motors (IPMSMs) and ensure a large output torque while minimizing torque ripple and core loss, the multi-objective optimization strategy should be employed. In this study, we took an 8-pole, 48-slot IPMSM as a specimen. First, the width and thickness of the permanent magnet (PM) and the rotor bridge structures were pre-selected as optimization parameters, while torque ripple and core loss were taken as optimization targets. Then, the Taguchi method to perform orthogonal experiments was employed to select the multi-parameter combinations that make the experimental results stable and with little fluctuation. To ensure the optimal results, the function equations were obtained by multivariate nonlinear fitting, while the parameters were optimized by particle swarm optimization (PSO). Finally, the optimal results were verified by the Finite Element Method (FEM). The results show that our proposed hybrid method can provide an optimal design strategy with better performance such as smaller torque ripple and core loss while maintaining a larger output torque.

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Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering

Cited by 12 publications

References 23 publications

Influence of a Winding Short-Circuit Fault on Demagnetization Risk and Local Magnetic Forces in V-Shaped Interior PMSM with Distributed and Concentrated Winding

Influence of a Winding Short-Circuit Fault on Demagnetization Risk and Local Magnetic Forces in V-Shaped Interior PMSM with Distributed and Concentrated Winding

An Improved Bearing Fault Diagnosis Model of Variational Mode Decomposition Based on Linked Extension Neural Network

Multi Objective Optimization of Permanent Magnet Synchronous Motor Based on Taguchi Method and PSO Algorithm

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