Adaptive ordinal pattern based mode decomposition guided by ordinal structural entropy for rotating machinery fault diagnosis

Abstract

Extracting weak and compound fault features from rotating machinery signals remains challenging because strong noise and multi-source modulation severely degrade the reliability of conventional decomposition methods. This study proposes an ordinal structure-guided adaptive ordinal pattern (OP) based mode decomposition framework, referred to as AOPMD, which introduces an ordinal structural entropy (OSE) criterion to quantitatively characterize the periodic regularity of impulsive fault transients. By exploiting anchor sequence consistency, the OSE criterion enables adaptive selection of key decomposition parameters and robust identification of fault-related modes, thereby alleviating the empirical parameter dependence inherent in conventional OP based mode decomposition. Numerical simulations and experimental studies on bearing faults, gear faults, and bearing and gear compound faults demonstrate that the proposed method achieves clearer fault feature separation and improved interpretability compared with representative decomposition techniques. These results indicate that the proposed AOPMD offers a robust and physically interpretable solution for compound fault diagnosis in rotating machinery under complex operating conditions.