RESEARCH PAPER
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
1
Beijing Jiaotong University, China
2
Beijing Institute of Petrochemical Technology, China
3
Tsinghua University, China
Submission date: 2025-02-17
Final revision date: 2025-03-30
Acceptance date: 2025-06-05
Online publication date: 2025-06-08
Publication date: 2025-06-08
Eksploatacja i Niezawodność – Maintenance and Reliability 2026;28(1):205977
HIGHLIGHTS
- LESSECT enhances time-frequency resolution for non-stationary signals.
- LESSECT excels in resolving closely spaced, non-proportional instantaneous frequencies.
- LESSECT overcomes energy leakage and blurring issues in traditional TFA techniques.
- LESSECT improves the reliability of fault detection in rotating machinery systems.
KEYWORDS
fault diagnosissystem reliabilityTime-frequency analysisnon-stationary signalsnon-proportional instantaneous frequency
TOPICS
ABSTRACT
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.
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