Bearing compound fault diagnosis model based on semantic fusion zero sample learning

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Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (10) : 278-286.

FAULT DIAGNOSIS ANALYSIS

Bearing compound fault diagnosis model based on semantic fusion zero sample learning

LI Yaohua, ZHAO Jia*， ZHANG Xinjie

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Abstract

To address the challenges of fault type coupling and data acquisition for rolling bearings in complex environments, researchers proposed a bearing compound fault diagnosis model based on semantic fusion zero sample learning. During training, a Semantic Autoencoder (SAE) establishes a link between visual space and semantic space, mitigating the domain migration issue. In testing, the model identifies unknown faults through similarity calculations. This approach introduces a semantic fusion encoding strategy, transforming the vibration amplitude and frequency characteristics of bearing faults into distinct semantic representations. This strategy retains extensive physical information and enhances semantic differences among fault types by fusing this data, thus significantly boosting the accuracy of composite fault classification. Moreover, the integration of a Convolutional Neural Network (CNN) with Adaptive Margin Center Loss (AMCL) optimizes fault feature extraction, capturing compound fault characteristics of bearings more accurately. Experimental results indicate an accuracy of 87.96%, surpassing that of the comparison model.

Key words

Rolling bearing / Fault diagnosis / Semantic fusion / Zero sample learning / Semantic autoencoder

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LI Yaohua, ZHAO Jia, ZHANG Xinjie . Bearing compound fault diagnosis model based on semantic fusion zero sample learning[J]. Journal of Vibration and Shock, 2025, 44(10): 278-286

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