Novel Anchor Discrimination Learning for Physics-Informed Machine Degradation Modeling

Machine degradation modeling is an enabling methodology to use monitoring data to evaluate machine health conditions. Fault detection needs to confirm whether there exists an incipient fault in a machine while machine diagnostics require knowing where the fault occurs and checking a specific fault type. In this article, an anchor discrimination learning model (ADLM) for physics-informed machine degradation modeling is innovatively proposed to find a projection direction that minimizes a distance between an anchor and samples with a same label of the anchor, and simultaneously maximizes a distance between the anchor and samples with a different label of the anchor. Subsequently, the ADLM is mathematically derived and formulated as a generalized Rayleigh quotient. Instead of using hand-crafted features, this article directly inputs normal and abnormal raw square envelope spectra into the ADLM for machine degradation modeling and the responses of the ADLM, namely an optimal direction, can automatically localize informative frequency components for immediate machine fault detection and diagnostics. Unlike most data-driven methodologies, the proposed methodology is physics-informed and its outputs are capable of indicating physical fault frequencies and their relevant frequency bands for quick fault detection and diagnostics. Two experimental studies are conducted to verify the feasibility of the proposed ADLM for machine degradation modeling.