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Unsupervised construction of health indicator for rotating machinery via multi-criterion feature selection and attentive variational autoencoder

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Science China(Technological Sciences) 2024年第5期67卷 1524-1537页

作者： LI XinYu CHENG ChangMing PENG ZhiKe State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai200240China School of Mechanical Engineering Ningxia UniversityYinchuan750021China

Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating *** interference from artificial labeling,the HI construction approaches in an unsupervised manner have attracted substantial ***,current unsupervised methods generally struggle with two problems:(1)ignorance of both redundancy between features and global variability of features during the feature selection process;(2)inadequate utilization of information from different sampling *** tackle these problems,this work develops a novel unsupervised approach for HI construction that integrates multi-criterion feature selection and the Attentive Variational Autoencoder(Attentive VAE).Explicitly,a multi-criterion feature selection(Mc FS)algorithm together with an elaborately designed metric is proposed to determine a superior feature subset,considering the relevance,the redundancy,and the global variability of features ***,for the adequate utilization of the information from distinct sampling moments,a deep learning model named Attentive VAE is *** Attentive VAE is solely fed with the selected features in the health state for model training and the HI is derived through the reconstruction error to reveal the degradation degree of *** case studies based on genuine experimental datasets are involved to quantitatively evaluate the superiority of the developed approach,demonstrating its superiority over other unsupervised methods for characterizing degradation *** effectiveness of both the Mc FS algorithm and the Attentive VAE is verified by ablation experiments,respectively.

关键词： health indicator(HI) unsupervised learning multi-criterion feature selection global variability attention mechanism

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