Local Entropy Selection Scaling-extracting Chirplet Transform for Enhanced Time-Frequency Analysis and Precise State Estimation in Reliability-Focused Fault Diagnosis of Non-stationary Signals

作者：Shaodan Zhi, Yueyang Niu, Limei Ma, Hengshan Wu, Haikuo Shen, Tianyang Wang · 发表于：Eksploatacja i Niezawodnosc - Maintenance and Reliability · 年份：2025 · DOI：10.17531/ein/205977 · 被引用次数：29 · 研究领域：Fault Detection and Control Systems、Machine Fault Diagnosis Techniques、Ultrasonics and Acoustic Wave Propagation

Under diverse conditions, the vibration signals of complex rotating machinery exhibit non-stationary behavior, multi-component characteristics, closely spaced frequencies, and non-proportionality, posing challenges to conventional time-frequency analysis (TFA) methods. These limitations hinder accurate instantaneous frequency (IF) estimation and time-frequency representation (TFR) construction, directly impacting machinery fault diagnosis. As such, we propose the Local Entropy Selection Scaling-Extracting Chirplet Transform (LESSECT), which optimizes entropy-based chirp rate (CR) selection to match non-proportional fundamental frequencies. By adaptively selecting multiple CRs at the same time center, LESSECT enhances TFR resolution and energy concentration, leading accurate IF identification. Experimental validation on bat echolocation, bearing fault, and planetary gearbox signals shows its superior performance in resolving non-proportional, closely spaced IFs. This significantly improves state estimation and enhances machinery diagnostics, contributing to greater system reliability.