《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

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Diaphragm pump check valve fault diagnosis method based on supervised contrastive learning and hybrid attention ResNet

Published:2024-04-24 author:REN Hongbing, PENG Yuming, HUANG Haibo. Browse: 1034 Check PDF documents

Diaphragm pump check valve fault diagnosis method based on supervised

contrastive learning and hybrid attention ResNet

REN Hongbing1, PENG Yuming1, 2, HUANG Haibo1, 2

(1.Institute of Automotive and Energy Power, Southwest Jiaotong University, Chengdu 610036, China;

2.Engineering Research

Center of Advanced Drive Energy-saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu 610036, China)

Abstract: In the industrial production environment, strong noise and other environmental stimuli result in similarities in the characteristics of different faults in the diaphragm pump check valve, making it difficult for traditional deep learning methods to accurately identify the valve's fault status. To solve this problem, a method combining supervised contrastive learning and hybrid attention residual neural network (HA-ResNet) was proposed for fault diagnosis of diaphragm pump check valves. Firstly, the attention mechanism was introduced into the residual neural network to enhance the network's learning capabilities. The weights of important but weak features were adaptively adjusted by the attention mechanism, and the suppression of useful information through identity transformations was reduced. Secondly, a weighted “supervised contrastive loss(SCL) + cross-entropy (CE)loss” was proposed to adjust the distances between different fault states of the check valve. The classification boundaries for different fault states were clarified by this method and the interference of noise or environmental stimuli was reduced. Finally, the effectiveness and stability of the method combining supervised contrastive learning and HA-ResNet were verified using real measurement data. The results show that the average accuracy of supervised contrastive learning and HA-ResNet fusion method reaches 99.3% on the validation set of the diaphragm pump check valve. Comparing with other fault diagnosis methods, supervised contrastive learning and HA-ResNet fusion method has advantages in diagnostic accuracy and stability. The reliability of this method for fault diagnosis under noise interference conditions has been verified.

Key words: diaphragm pump; check valve; fault diagnosis; supervised contrastive loss(SCL); hybrid attention residual neural networks(HA-ResNet); feature similarity; deep learning method

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