Multi-Objective Sparsity Maximum Mode De-Composition: A New Method for Rotating Machine Fault Diagnosis on High-Speed Train Axle Box

Abstract

Signal decomposition techniques generally have two limitations. First, for adaptive correlated kurtogram (ACK) and variational mode decomposition (VMD), etc., the definition of the decomposition target has no direct relationship with the features of fault impacts, i.e., the decomposition is not guided by the health indices used to characterize bearing faults, resulting in fault impacts cannot be completely separated from vibration data. Second, it is hard to accurately determine the decomposition stop parameter (the number of final output modes), resulting in frequent over- or under-decomposition. All these are not conducive to bearing fault detection in engineering practice. In this paper, a new decomposition method, multi-objective sparse maximum mode decomposition (MOSMMD), is proposed. Firstly, by analyzing the Fourier spectrum of vibration data, MOSMMD constructs multiple finite impulse response filters covering different frequency bands to obtain initial candidate modes. Then, aiming at maximizing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L2</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L1</i> norm of envelope spectrum (ES), the upper and lower cutoff frequencies of each filter are adjusted adaptively through iterative optimization, and the frequency bands covered by each mode are adjusted accordingly. Finally, by using that fault feature harmonics have periodic natures in ES, the redundant modes are eliminated and only the modes with obvious fault features are output. Compared with VMD and ACK, the decomposition target of MO-SMMD is directly related to the fault features, i.e., cyclostationarity of fault impacts, which makes the extraction of fault impacts more accurate. Moreover, the number of final output modes of MOSMMD is determined adaptively and does not need to be set in advance as an input parameter. Also, MOSMMD can decompose fault modes from vibration data without prior fault information, even when processing concurrent fault signals. Simulation and experiments demonstrate the superiority of the proposed MOSMMD.