A Comprehensive Interturn Fault Severity Diagnosis Method for Permanent Magnet Synchronous Motors Based on Transformer Neural Networks

Parvin, Farbod; Faiz, Jawad; Yuan, Qi; Kalhor, Ahmad; Akin, Bilal

doi:10.1109/tii.2023.3242773

Cited by 11 publications

(1 citation statement)

References 33 publications

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“…Many PMSM monitoring and diagnosis methods have been proposed by researchers, which can be summarized succinctly as being analytical model-based, signal-based, or datadriven. Parvin et al [3] proposed an ITSC fault diagnosis method based on transfer learning using stator currents under alpha-beta reference system as inputs to the network, which was able to diagnose the fault severity and short circuit current with both accuracy above 96%. Mohammad-Alikhani et al [4] proposed a generalized fault diagnosis method based on LSTM regulated deep residual network, which was evaluated with the ITSC test of electric motors and the Case Western Reserve University bearing fault dataset, and achieved 100% accuracy on both datasets.…”

Section: Introductionmentioning

confidence: 99%

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

Yan,

2024

Meas. Sci. Technol.

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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.

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Section: Introductionmentioning

confidence: 99%