A novel dictionary learning approach based on blind source separation basis and its application

Abstract

The early recognition of wheel wear is an important task to the safe and efficient operation of a railway network. This article presents a new dictionary learning approach for wheel condition monitoring based on an adaptive parametric algorithm of blind source separation and extending K-means and singular value decomposition algorithm. Numerical simulations confirm the effectiveness of the proposed method. An experiment of wheel condition monitoring is conducted using a JD-1 wheel/rail simulation facility. Data calculation and theoretical analysis of wheel-rail contact dynamic show that the proposed method can adaptively learn and accurately identify wheel defects and verify the performance of the proposed method.