No‐load losses based method to detect demagnetisation fault in permanent magnet synchronous motors with parallel branches

Hou, Zhaowen; Huang, Jin; Liu, He; Liu, Zijian; Ye, Ming; Yang, Junyou

doi:10.1049/iet-epa.2016.0625

Cited by 6 publications

(3 citation statements)

References 29 publications

(47 reference statements)

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“…In [23], the existing detection techniques for rotor failure bar faults are reviewed and a comprehensive list of references is given for each fault feature. Hou et al [24] defined an index, that is, the ratio of the increased loss to the total loss of the motor under no-load condition, to monitor the condition of the rotor magnet. This approach is limited to the special case of demagnetization fault detection in a permanent magnet synchronous motor.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

Xu,

Li,

Liu

et al. 2023

Meas. Sci. Technol.

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Fault diagnosis of asynchronous motors has become a pressing need in the metallurgical industry. Due to the complex structure of asynchronous motors, fault types and fault characteristics are diverse, with strong nonlinear relationships between them, which leads to the difficulty of fault diagnosis. To efficiently and accurately diagnose various motor faults, we propose a fault diagnosis method based on an optimal deep bidirectional long short-term memory neural network. First, the three-phase current, multidimensional vibrational signal, and acoustic signal of the asynchronous motor are collected and construct diverse and robust data sample set to enhance the generalization ability of the model. Next, a modified 3D logistics-sine complex chaotic map (3D LSCCM) is constructed to improve the global and local search capabilities of the pigeon swarm optimization algorithm (PIO). Then, we construct a deep bidirectional long short-term memory network (Bid-LSTM) with attention mechanism to mine high-value fault characteristic information. Meanwhile, the optimal hyper-parameters of the deep ABid-LSTM are explored using the modified PIO to improve the training performance of the model. Finally, the fault data samples of asynchronous motor are induced to train and test the proposed framework. By fusing diverse data samples, the proposed method outperforms conventional deep Bid-LSTM and achieves fault diagnosis accuracy of 99.13%. It provides a novel diagnostic strategy for motor fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

Xu,

Li,

Liu

et al. 2023

Meas. Sci. Technol.

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show abstract

“…Poor thermal properties make it susceptible to irreversible demagnetization. Therefore, research to inhibit irreversible demagnetization caused by a low coercive force has recently been studied [9][10][11][12][13][14][15]. For example, prior research, such as studies to minimize irreversible demagnetization by changing the rotor shape [16,17] by modifying the volume and thickness of the magnet and by adjusting the number of poles and slots, are underway.…”

Section: Introductionmentioning

confidence: 99%

Irreversible Demagnetization Improvement Process of Hybrid Traction Motors with Dy-Free Magnets

Song

Pyo²,

Nam³

et al. 2022

Machines

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Permanent magnet synchronous motors (PMSMs), with their high power density, high efficiency, and thermal stability, are widely used nowadays. PMSM magnets are composed of rare earth elements. However, rare earth elements are subject to severe price fluctuations because of their limited availability and the monopoly of some countries. Therefore, extensive research on magnets devoid of rare earth elements has been conducted recently. Although a magnet devoid of rare earth elements has a high irreversible demagnetization rate at high temperatures owing to its low coercive force, an irreversible demagnetization improvement process is proposed in this paper to compensate for this disadvantage. This process analyzes the contribution of the magnet’s back EMF (electromotive force) using the flux linkage equation, which does not change with time. Next, the location of irreversible demagnetization is moved to a position with a low contribution to the back electromotive force. Consequently, even if irreversible demagnetization occurs at the same size, the irreversible demagnetization ratio is reduced. The proposed process can minimize irreversible demagnetization while maintaining performance.

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“…Improvement of fault tolerance or availability of an electrical drive containing a three-phase 12 stator teeth/10 rotor poles (12/10) the flux-switching PM machine is given in [12]. Effects of the partial demagnetisation fault are studied in [13], when the motor is under no-load condition, to monitor the condition of the rotor magnets.…”

Section: Introductionmentioning

confidence: 99%

Health monitoring and prognosis of electric vehicle motor using intelligent‐digital twin

Venkatesan

Manickavasagam

Tengenkai

et al. 2019

IET Electric Power Applications

118

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Electric mobility has become an essential part of the future of transportation. Detection, diagnosis and prognosis of fault in electric drives are improving the reliability, of electric vehicles (EV). Permanent magnet synchronous motor (PMSM) drives are used in a large variety of applications due to their dynamic performances, higher power density and higher efficiency. In this study, health monitoring and prognosis of PMSM is developed by creating intelligent digital twin (i-DT) in MATLAB/ Simulink. An artificial neural network (ANN) and fuzzy logic are used for mapping inputs distance, time of travel of EV and outputs casing temperature, winding temperature, time to refill the bearing lubricant, percentage deterioration of magnetic flux to compute remaining useful life (RUL) of permanent magnet (PM). Health monitoring and prognosis of EV motor using i-DT is developed with two approaches. Firstly, in-house health monitoring and prognosis is developed to monitor the performance of the motor in-house. Secondly, Remote Health Monitoring and Prognosis Centre (RHMPC) is developed to monitor the performance of the motor remotely using cloud communication by the service provider of the EV. The simulation results prove that the RUL of PM and time to refill the bearing lubricant obtained by i-DT twins theoretical results.

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No‐load losses based method to detect demagnetisation fault in permanent magnet synchronous motors with parallel branches

Cited by 6 publications

References 29 publications

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

Irreversible Demagnetization Improvement Process of Hybrid Traction Motors with Dy-Free Magnets

Health monitoring and prognosis of electric vehicle motor using intelligent‐digital twin

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