استخراج چیرپلت مورلت سه بعدی بمنظور تشخیص عیوب سیستمهای چرخدنده‌ای

Vibration signals serve as a key tool in fault detection for rotating machinery, providing valuable information regarding the health status and performance of mechanical components. Despite advancements in time-frequency analysis methods, major challenges such as frequency overlap in signals and the presence of severe noise in real-world environments continue to hinder accurate detection. In this study, a novel method titled as Three Three-Dimensional Synchro-Extracted Morlet Chirplet Transform is introduced, which analyzes the signal across the dimensions of time, frequency, and frequency change rate (chirp rate). This approach not only exhibits higher robustness to noise compared to existing methods but also enables complete separation of frequency components with high overlap, including signals with crossing curves in the time-frequency plane. These advantages, combined with the capability to analyze non-stationary signals, render this method an ideal option for fault diagnosis in rotating machinery, particularly during the startup phase—where variable rotational speeds lead to signal non-stationarity, speeds below nominal cause proximity of component frequencies, and industrial noise predominates. Results from processing simulated and real signals acquired from a gearbox with gear faults demonstrate the remarkable performance of this method in fault detection.