Incomplete Data Driven Intelligent Compound Fault Diagnosis Method for Machinery

doi:10.3901/JME.2024.24.045

Abstract

Abstract: Data driven fault diagnosis methods have significant advantages in mass data processing, discriminative feature learning and precision pattern recognition, which bring not only new opportunities for intelligent maintenance of machinery but also unsolved challenges：① The effectiveness and reliability of current data driven methods depend on the completeness of labelled fault data, whereas, in industrial scenarios, it is the often case that the label or repair information is missing, the fault data is lacking and the availability of the monitoring data is low；② Compound faults are common faults in equipment, resulting in the difficulty of intelligent fault diagnosis increasing dramatically when the compound fault data are unavailable, but there are few studies that focus on the interpretability of intelligent compound fault diagnosis models. To address the above challenges, an incomplete data driven intelligent compound fault diagnosis method, named wavelet capsule network, is proposed for industrial equipment. First, the wavelet capsule network is constructed by combining the wavelet kernel convolutional layer (WKL) and the capsule layers. Specifically, WKL is used to endow the model with the ability to extract interpretable features from vibration signals, and the capsule layers are introduced to decouple compound faults. Second, the wavelet capsule network is trained by incomplete fault data (just contains normal and single fault samples, and lacks compound fault samples). Finally, an experiment carried out on a five-speed transmission is used to validate the effectiveness of the proposed method. Furthermore, the interpretability analysis is performed on the features extracted by the proposed model to improve the credibility of the diagnosis result. The experimental results show that the proposed method achieves higher diagnosis accuracy than compared methods and the features extracted by the model are interpretable to a certain extent which might provide a novel solution to decoupling the compound fault with incomplete data.

Key words: compound fault diagnosis, incomplete data, wavelet kernel convolution, capsule network, interpretability analysis

CLC Number:

TH17
TP183

LI Weihua, LAN Hao, CHEN Zhuyun, HUANG Ruyi. Incomplete Data Driven Intelligent Compound Fault Diagnosis Method for Machinery[J]. Journal of Mechanical Engineering, 2024, 60(24): 45-55.

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