Intelligent health evaluation of rolling bearings based on subspace meta-learning

Abstract

Health evaluation is attracting more and more attention in the domain of machinery prognostic and health management (PHM). Meanwhile, few studies have been devoted to health evaluation under variable working conditions and few shots learning, which are common situations under industrial sites. Thus, this shortcoming becomes the motivation of our study. We propose subspace meta-learning (SML) that integrates the strengths of knowledge transfer, constructing the statistically relevant latent subspace, and meta learning, realizing few shots prognostics. To be specifically, time-frequency images are first extracted with sliding windows along with the vibration signals across different life experiments of rolling bearings. Then, two-dimensional domain adaptation based on high order statistical properties is utilized to construct latent subspace and generate meta degradation knowledge. Finally, the convolutional layer based meta learning under model-agnostic learning mode is set up based on the time-frequency degradation knowledge. For a transparent test of our proposed SML health evaluation methodologies, public FEMTO-ST bearing datasets are employed for verifications, and comparisons are also conducted between existing prediction methods. Prediction performances reveal that the superiority of SML under few-shot prognostics.