基于DTCNN-SVM的工业循环水系统供水泵故障诊断

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Abstract The working state of the water supply pump in the industrial circulating water system is an important factor affecting the safe production of the industrial process. In order to identify the working state of the water supply pump timely and accurately, a fault diagnosis method based on deep transfer convolutional neural network and support vector machine (DTCNN-SVM) was proposed. Firstly, the vibration signal which is strongly related to the working state is preprocessed to realize the two-dimensional grayscale image of the vibration time series signal. On this basis, the deep transfer convolutional neural network model which combined transfer learning and residual neural network is used to extract the grayscale image features of the vibration signal, and the deep learning features are reduced based on the fuzzy inconsistency measurement. Finally, the support vector machine method is used to establish the fault diagnosis model of water supply pump. The experimental results show that the proposed method can effectively identify the working state of the water supply pump with a small amount of sample data and model parameters.

Key words： water supply pump fault diagnosis deep transfer convolutional neural network support vector machine fuzzy inconsistency measurement

Received: 27 January 2022 Published: 15 July 2023

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	WU Jia
	LI Mingchen
	TANG Wenyan

Cite this article:

WU Jia,LI Mingchen,TANG Wenyan. Fault diagnosis of water supply pump in industrial circulating water system based on DTCNN-SVM[J]. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(13): 226-234.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I13/226

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