Scale-Compensation Community Distance Entropy: A Novel Feature Extraction Tool for Fault Identification of Rotating Machinery

Fault identification plays a pivotal role in condition-based maintenance of rotating machinery, with identification accuracy highly dependent on the quality of extracted features. Multiscale permutation entropy (PE) methods have emerged as promising feature extraction tools due to the fast computation of PE and informative scalability of multiscale procedures. However, PE is unresponsive to amplitude variation due to the binary orbit similarity state, and the multiscale procedure suffers from scale information loss or even scale absence, all of which decrease the identification accuracy. To address these issues, this article proposes a novel approach termed the scale-compensation community distance entropy (SCDE) method for fault identification. On one hand, the community distance-based orbit similarity value is put forward to diversify orbit similarity states, achieving a dual-characteristic perception of both frequency and amplitude changes. On the other hand, the scale-compensation procedure is proposed to enrich overall and detailed information on continuous scales. The efficiency and superiority of SCDE are rigorously demonstrated using simulation data and experimental datasets.