التفاصيل البيبلوغرافية
| العنوان: |
Demagnetization Fault Diagnosis of a PMSM for Electric Drilling Tools Using GAF and CNN. |
| المؤلفون: |
Zhang, Qingxue, Cui, Junguo, Xiao, Wensheng, Mei, Lianpeng, Yu, Xiaolong |
| المصدر: |
Electronics (2079-9292); Jan2024, Vol. 13 Issue 1, p189, 18p |
| مصطلحات موضوعية: |
FAULT diagnosis, CONVOLUTIONAL neural networks, AIR gap flux, ELECTRIC drills, DEMAGNETIZATION, PERMANENT magnet motors, HOUGH transforms |
| مستخلص: |
Permanent magnets (PMs) provide high efficiency for synchronous motors used for driving drilling tools. Demagnetization is a special fault that reduces the efficiency of the permanent magnet synchronous motor (PMSM) and thus affects the performance of the drilling tools. Therefore, early detection of demagnetization is important for safe and efficient operation. However, it is difficult to detect multiple demagnetization types at the same time using traditional fault diagnosis methods, and the recognition accuracy cannot be guaranteed. To solve the above problem, this article proposes a method combining Gramian angular field (GAF) transform and convolutional neural network (CNN) to recognize and classify different types of demagnetization faults based on output torque signal. Firstly, the thermal demagnetization model of PM was obtained by experiments, and the finite element model (FEM) of PMSM for electric drilling tools was established to analyze the torque, back electromotive force (BEMF), and air gap flux density under different demagnetization faults. Then, the acquired one-dimensional torque signals were transformed into two-dimensional gray images based on the GAF method to enhance the fault features. To improve the generalization ability of the CNN, these gray images were augmented through increasing noise. Finally, the CNN structure was designed and trained with a training accuracy of 98.33%, and the effectiveness of the method was verified by the demagnetization fault experiment. The results show that the testing accuracy of the CNN model was 97.41%, indicating the proposed method can diagnose various demagnetization faults effectively, and that it is immune to loads. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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