Improved particle swarm optimization-based adaptive multiresolution dynamic mode decomposition with application to fault diagnosis of rolling bearing

Abstract

It is very important to detect fault and extract fault features of mechanical systems at an early stage, because the above two steps promise normal operation of mechanical systems. However, they are also very challenging. In this context, this article has put forward improved particle swarm optimization-based adaptive multiresolution dynamic mode decomposition of rolling bearing (IPSO-AMDMD). Multiresolution dynamic mode decomposition (MRDMD) is used to decompose signals of rolling bearing at the early stage, multiscale fuzzy entropy (MFE) is employed to divide low-rank components and sparse components. In order to make up for the shortcomings of the above two methods, namely truncated rank of MRDMD and inaccurate selection in threshold of MFE, this paper has proposed a new fitness function, which is called synthetic envelope kurtosis characteristic energy difference ratio, and adopted the improved particle swarm optimization algorithm (IPSO) to select the optimal parameters adaptively. With these two steps, signals can be decomposed perfectly. Finally, reconstructed signals, which are obtained through the combination of signals from each layer according to a certain weight, go through DMD again, thus getting the final recovered signal. Through simulation experiment and in-field experiment, it has proved that IPSO-AMDMD is viable and sound in accurately extracting features from fault signals.