Rolling Bearing Fault Feature Extraction Using Local Maximum Synchrosqueezing Transform and Global Fuzzy Entropy

To achieve good performance of fault feature extraction for a rolling bearing, a new feature extraction method is presented in this paper based on local maximum synchrosqueezing transform (LMSST) and global fuzzy entropy (GFuzzyEn). First, targeting the time-varying features of the vibration signals of the rolling bearing, the LMSST algorithm, which is a newly developed time-frequency method and allows for adaptive mode decomposition, is used to preprocess the vibration signals into a number of mode components. Then, as a modification of FuzzyEn, GFuzzyEn is adopted to evaluate the complexity of these mode components. Compared to FuzzyEn, which focuses mainly on the local characteristics of the short-term physiological time series, the GFuzzyEn emphasizes the global characteristics of the signal considering that the bearing vibration signals' global fluctuation may change as the bearing works under various conditions. Finally, the fault features of the bearing vibration signals are extracted by combining the LMSST and the GFuzzyEn. The experimental analysis shows that the proposed LMSST-GFuzzyEn method can extract rich fault-related information from the bearing vibration data and can achieve good classification performance for rolling bearing fault diagnosis.