Ensemble ordinal pattern mode decomposition based on ICS2 blind filter for rotating machinery fault diagnosis

Abstract

Ordinal pattern (OP) mode decomposition (OPMD) is a novel signal decomposition method that can effectively reveal pseudo-periodic impulse components obscured by noise. However, OPMD inherently relies on the precision of OP anchor points, which diminish its performance in situations with a low signal-to-noise ratio. Moreover, the accuracy of OPMD can be also significantly compromised in the presence of periodic impulse interference. To enhance the applicability of OPMD in mechanical fault diagnosis, this article proposes an ensemble OPMD method, incorporating blind filter based on indicators of second-order cyclostationary (ICS2). First, the ICS2 blind filtering technique is proposed to mitigate excessive noise spectral line and periodic interference caused by the transmission path and the inherent vibrations of the equipment and a filtering signal based on the ICS2 blind filtering technique is utilized to identify anchors of potential periodic OPs. Subsequently, the phase-rectified signal averaging (PRSA) filter is designed using PRSA technique based on these anchors. Finally, a filter bank is constructed based on the ICS2 filter and the PRSA filter to decompose it into fault-related impulse patterns and nonimpulse residual patterns. To validate the effectiveness of the ensemble OPMD method for fault diagnosis, a simulation experiment and three case studies involving bearing and gear faults under typical operating conditions were conducted.