Multivariate Dynamic Mode Decomposition and Its Application to Bearing Fault Diagnosis

Abstract

In practical engineering applications, the multivariate signal contains more fault feature information than the single-channel signal. How to realize synchronous extraction of fault features from the multivariate signal is of great significance in fault diagnosis of rotary machinery. Dynamic mode decomposition (DMD) has attracted much attention due to its excellent dynamic feature extraction ability. However, DMD lacks mode aliasing property when dealing with the multivariate signal, which may lead to the loss of critical fault feature information. Cater to this problem, this article proposed a multivariate DMD (MDMD) algorithm that is the multivariate extension of DMD. First, snapshot tensors are defined to convert multivariate signals to tensor format. Then, the MDMD algorithm is proposed by introducing tensor operations into the original DMD algorithm, where a tensor low tubal rank component extraction framework is constructed to enable simultaneous extraction of bearing fault features from the multivariate signal, to enhance the robustness and effectiveness of the algorithm. Finally, both numerical simulations and experiments verify that the proposed MDMD has higher fault diagnosis accuracy than other multivariate signal-processing methods.