Unsupervised Locality-Preserving Robust Latent Low-Rank Recovery-Based Subspace Clustering for Fault Diagnosis

Abstract

With the increasing demand for unsupervised learning for fault diagnosis, the subspace clustering has been considered as a promising technique enabling unsupervised fault diagnosis. Although various subspace clustering methods have been developed to deal with high-dimensional and non-linear data, analyzing the intrinsic structure from the data is still challenging. To address this issue, a new subspace clustering method based on locality-preserving robust latent low-rank recovery (L2PLRR) was developed. Unlike conventional subspace clustering methods, the developed method maps the high-dimensional and non-linear data into a low-dimensional latent space by preserving local similarities of the data with the goal of resolving the difficulty in analyzing the high-dimensional data. Likewise, in the developed L2PLRR method, learned features correspond to low-rank coefficients of the data in the latent space, which will be further used for fault diagnosis (e.g., identification of health states of an object system). The efficacy of the developed L2PLRR method was verified with a bearing fault diagnosis application by comparing with conventional and state-of-the-art subspace clustering methods in terms of diagnostic performance.