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

Health indicator (HI) construction is a crucial task in degradation evaluation and facilitates the prognostic and health management (PHM) of rotating machinery. Excluding interference from artificial labeling, the HI construction approaches in an unsupervised manner have attracted substantial attention. Nevertheless, 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 moments. To 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 (McFS) 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 simultaneously. Then, for the adequate utilization of the information from distinct sampling moments, a deep learning model named Attentive VAE is established. The 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 machinery. Two 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 processes. The effectiveness of both the McFS algorithm and the Attentive VAE is verified by ablation experiments, respectively.