Mechanical Fault Diagnosis Method Based on Multi-task Twin Kernel Matrix Machine

doi:10.3901/JME.260415

Abstract

Abstract: Multi-object joint diagnosis often requires the establishment of multiple independent single task models, which leads to the omission of correlation information between multiple objects in the diagnostic model, resulting in inaccurate diagnostic models. Based on this, a mechanical fault diagnosis method based on multi-task twin kernel matrix machine (MTTKMM) is proposed. In MTTKMM, the synchronization processing of multi-task data is first achieved by designing kernel enhancement terms, which help to explore the correlation features between different tasks and improve the model's ability to utilize common information. Then, by utilizing the built-in nonlinear offset to capture the nonlinear relationships between data, the model becomes more flexible when dealing with complex data of multiple objects, further improving the stability of model classification. Finally, considering the significant differences in data between different tasks, a generalization loss is designed to optimize the model's fitting ability on multi-task data and reduce overfitting. To verify the effectiveness of MTTKMM in multi-task mechanical fault diagnosis, experimental verification is conducted using datasets such as rolling bearings and gears. The experimental results prove that MTTKMM has superior classification performance in multi-object diagnosis.

Key words: fault diagnosis, multi-task twin kernel matrix machine, multi-task learning, rolling bearing, gear

CLC Number:

TH133

PAN Haiyang, CHEN Chunan, ZHENG Jinde, TONG Jinyu, CHENG Jian. Mechanical Fault Diagnosis Method Based on Multi-task Twin Kernel Matrix Machine[J]. Journal of Mechanical Engineering, 2026, 62(9): 191-200.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260415

http://www.cjmenet.com.cn/EN/Y2026/V62/I9/191

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