L2-Norm Shapelet Dictionary Learning-Based Bearing-Fault Diagnosis in Uncertain Working Conditions

Abstract

Bearing-fault diagnosis is an important research topic and is crucial in modern industrial systems. Several methods for bearing-fault diagnosis have been developed. However, most existing methods can only detect faults under controlled working conditions or the variation of a single working condition (e.g., a varying load). When the working conditions change or multiple uncertain working conditions are encountered, the model performance is significantly degraded. Therefore, this paper proposes an L2-norm shapelet dictionary learning (SDL)-based bearing diagnosis method that aims to improve the accuracy of fault classification under uncertain working conditions. First, the L1-norm regularization term in the SDL algorithm is replaced with an L2-norm regularization to capture diversified shapelets from multiple varying working conditions. Then, a comprehensive bearing-fault diagnosis method is developed using the shapelet-transformation and support vector machine methods to handle the uncertainty of multiple working conditions. Two case studies using laboratory data and practical experiments were conducted to validate the proposed method. The experimental results for both the case studies indicate the effectiveness of the proposed method with regard to fault classification accuracy and visualization interpretability.