Bearing fault diagnosis based on flexible analytical wavelet transform and fuzzy entropy approach

Abstract

For fault detection of bearings to avoid unexpected downtime in rotating machinery (RM), their monitoring is continuously required. Whenever a fault develops in any of the rotating machinery components, the characteristics vibration signature of such a mechanical system will change. Revealing such characteristics in a vibration signature is the main aim of any signal processing technique. Wavelet transforms and its variants have been widely used for their ability to perform under different non-stationarity nature of vibration signals. Among them, a new variant known as Flexible analytic wavelet transform (FAWT) enjoys attractive characteristics such as flexible time-frequency (TF) covering, better shifting property in which the same amount of input shifted corresponds to the same amount of shifted output, and adjustable periodic nature of the wavelet, providing appropriate shape and wavelet window to meet the weak fault component frequencies. After that, features are extracted using fuzzy entropy (FzEn) to determine the complexity of these vibration signals. After fault feature extraction by fuzzy entropy (FzEn), several state-of-the-art classifiers are used to validate the effectiveness of the proposed method. The results show that the proposed method is advantageous in identifying the fault type and severity of bearings. © 2020 Elsevier Ltd. All rights reserved.