The multi-channel signals based tensor sparse representation classification method for fault diagnosis of high-speed train

Abstract

The transmission system is a key component to ensure the stable operation of high-speed trains. Thus, it is significant to monitor its condition to ensure the operation safety. Nowadays sparse representation is widely used in fault diagnosis. However, as the number of sensors is increasing, the existing method destroys the internal structure of multi-channel signals and cannot effectively deal with the fault diagnosis of multi-channel signals in parallel. Therefore, this article extends the existing sparse representation method to tensor space to extract the coupling information between channels and realize the fault diagnosis of multi-channel. First, a tensor sparse representation model is proposed to achieve data-level multi-channel signal fusion and complete inter-channel fault feature extraction. Then, a multimodal dictionary learning algorithm is proposed to adaptively design the data-driven dictionary to achieve data-driven feature extraction. Finally, a tensor sparse representation classification method is proposed to achieve the purpose of intelligent diagnosis. Fault experiments verify the effectiveness and superiority of the method.