Match-Extracting Chirplet Transform With Application to Bearing Fault Diagnosis

Abstract

As a typical non-stationary signal, rolling bearing fault signals exhibit dynamic characteristics and low signal-to-noise ratio, which lead to fuzzy spectrograms from traditional time-frequency analysis methods. It is a challenging task to accurately represent its instantaneous frequency trajectory. To solve this problem, this paper proposes a time-frequency analysis method which called match-extracting Chirplet transform for analyzing multi-component dynamic signals. It generates a time-frequency representation of high energy concentration by constructing a basis function that matches the Chirp rate with the original signal at any time-frequency point. A matching extraction operator is built to extract the best matching ridges on the instantaneous frequency trajectory to further improve the quality of the spectrum. The proposed method can effectively characterize the dynamic characteristics of multicomponent signals and reduce the energy divergence. Meanwhile the algorithm retains signal reconstruction and can be used to recover significant components by adaptively searching modal ridges. Numerical verification shows that this method has better performance in time - frequency location and noise robustness. Finally, the validity of this method in mechanical fault diagnosis is verified by applying it to actual bearing signals.