Analysis of Interpretability and Generalizability for Power Converter Fault Diagnosis Based on Temporal Convolutional Networks

doi:10.1109/TIM.2023.3280535

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	Analysis of Interpretability and Generalizability for Power Converter Fault Diagnosis Based on Temporal Convolutional Networks
	T. Ren, T. Han, Q. Guo and G. Li
刊名	IEEE Transactions on Instrumentation and Measurement
	2023
卷号	72
ISSN号	00189456
DOI	10.1109/TIM.2023.3280535
英文摘要	Despite the success of data-driven converter fault diagnosis methods, interpretability and generalizability limit the further promotion of data-driven methods in industrial applications. Therefore, to improve the accuracy in face of out-of-distribution problems and increase the confidence of power converter fault diagnosis, it is essential to understand the change and decision mechanism inside the deep model. First, we construct a general temporal convolutional network (TCN) to visualize the diagnostic process, which has been proven effective in power converter fault diagnosis. Then, the effect of hyperparameters on generalizability is analyzed under typical power converter disturbances. Finally, the concern area of the model for the current in the fault decision is interpreted intuitively by gradient-weighted class activation mapping (Grad-CAM), and the feature maps generated by the different channels are analyzed from multiple perspectives. The visualization results help to understand the complex structure of neural networks and can support the design of model to improve generalizability. © 1963-2012 IEEE.
URL标识	查看原文
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/67814]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	T. Ren, T. Han, Q. Guo and G. Li. Analysis of Interpretability and Generalizability for Power Converter Fault Diagnosis Based on Temporal Convolutional Networks[J]. IEEE Transactions on Instrumentation and Measurement,2023,72.
APA	T. Ren, T. Han, Q. Guo and G. Li.(2023).Analysis of Interpretability and Generalizability for Power Converter Fault Diagnosis Based on Temporal Convolutional Networks.IEEE Transactions on Instrumentation and Measurement,72.
MLA	T. Ren, T. Han, Q. Guo and G. Li."Analysis of Interpretability and Generalizability for Power Converter Fault Diagnosis Based on Temporal Convolutional Networks".IEEE Transactions on Instrumentation and Measurement 72(2023).