Faster Multiscale Dictionary Learning Method With Adaptive Parameter Estimation for Fault Diagnosis of Traction Motor Bearings

Abstract

Traction motor bearings are crucial components to guarantee stable operation. Thus, it is significant to monitor the bearing condition. Dictionary learning is a powerful method to extract the characteristic condition. Compared with ordinary dictionary learning, the multiscale dictionary learning method applied to transform coefficients performs well in extracting the impact signals and takes less learning time. However, it spends more time tuning parameters to make the algorithm more efficient, especially in practical industrial applications. Hence, a faster adaptive parameter multiscale dictionary learning (Faster AP-MSDL) method is proposed in this article which adaptively chooses the scales of learning and estimates the parameters of sparse coding in dictionary learning simultaneously, and it possesses two core traits including less learning time and adaptive parameter estimation, which make this method more suitable to practical industrial application. Finally, a simulation experiment and two practical industrial experiments including a bearing run-to-failure test and a fault experiment of traction motor rolling bearing are conducted using our proposed method to verify adaptive ability and superiority. The superiority of the proposed method is also verified by comparing with other state-of-the-art methods.