Intelligent diagnosis method for rotating machinery based on multi-physics field signal transfer correlation analysis

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (22) : 332-338.

SUN Yuanli1,2, SONG Zhihao2

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Abstract

Aiming at the problems that in the traditional fault diagnosis of rotating machinery across working conditions, only a single physical field signal is used to fully extract domain invariant features and the difficulty of improving the accuracy of cross-working condition diagnosis, an intelligent multi-physics field signal transfer correlation analysis is proposed. Fault diagnosis method. The multiphysics signal fusion strategy of convolution correlation analysis proposed by this method can effectively extract the spatial characteristic representation between multiphysics signals, and optimize and reduce the characteristic correlation matrix of multiphysics signals between different data domains by using the maximum mean difference At the same time, the feature correlation matrix sequence is input into the constructed long-term short-term memory neural network to extract the temporal correlation characteristics of the signal. This method can fully extract the spatial and temporal correlation characteristics of multi-physics field signals, effectively improving the accuracy of transfer diagnosis. Spend. The method is verified by using the collected data on the pump fault simulation test bench built. The results show that the proposed method can fully extract the domain invariant characteristics of multi-physics field signals, and obtain better diagnostic results in cross-condition fault diagnosis tasks.

Key words

multiphysics signals / correlation analysis / transfer learning / fault diagnosis

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SUN Yuanli1,2, SONG Zhihao2. Intelligent diagnosis method for rotating machinery based on multi-physics field signal transfer correlation analysis[J]. Journal of Vibration and Shock, 2023, 42(22): 332-338

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