Multi-fault diagnosis method for centrifugal pumps based on EMD-AE optimal selected features under strong noise conditions

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (23) : 66-74.

XIANG Mingsheng1,2, FENG Kun1,2, JIA Shaohui3, ZHAO Yan2,4

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Abstract

Noise frequently interferes with the fault diagnosis of industrial centrifugal pumps. To address this issue, this paper introduces a feature selection method that combines empirical mode decomposition and an autoencoder. Initially, effective time-frequency features are identified using compensation distance evaluation technology. Subsequently, modal components, which contain various scale and frequency features, are derived through empirical mode decomposition. The component with significant energy ratio variation is selected as the effective analysis component, from which effective features are extracted. These features are then concatenated to construct high-dimensional deep features. An autoencoder is subsequently utilized to reduce the dimensionality of these deep features, further refining the selection process to isolate the final fault-sensitive features for comprehensive feature extraction. In this study, a support vector machine is employed as the fault diagnosis model. Comparative analysis of multiple fault data from industrial centrifugal pumps demonstrates that the accuracy of the proposed method surpasses traditional time-frequency feature-based approaches by 6.13%、7.46% and 12% under -5dB, -7dB, and -10dB strong noise conditions, respectively. This method exhibits robust noise resistance and effectively extracts sensitive equipment state features under noise interference.

Key words

strong noise / centrifugal pump / empirical mode decomposition / optimal selection feature / sensitive feature

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XIANG Mingsheng1, 2, FENG Kun1, 2, JIA Shaohui3, ZHAO Yan2, 4. Multi-fault diagnosis method for centrifugal pumps based on EMD-AE optimal selected features under strong noise conditions[J]. Journal of Vibration and Shock, 2024, 43(23): 66-74

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