Research on Fault Diagnosis of Rolling Bearing based on Synchrosqueezing Extracting Transform - Qi Liu; Yanxue Wang (Jixie chuandong, 2021)
Rolling bearings are important components of large machinery and play a very important role. When the bearing fails， if not repaired or replaced them in time， it will seriously affect the life of the equipment. Time-frequency analysis method is a very effective fault feature extraction tool， which has been widely used. Simultaneously， the energy concentration of time-frequency representation affects the effect of fault feature extraction， so a more concentrated time-frequency analysis method plays a vital role in mechanical signal processing and fault diagnosis. A novel time-frequency domain feature extraction method， synchrosqueezing extracting transform is proposed. This method mainly includes two steps， firstly， the majority of energy of the signal is collected into multiple small frequency bands by using a synchrosqueezing transform， which achieves the initial concentration and reduce the energy loss of the next step. Then， a frequency extracting operator is introduced into the results of the synchrosqueezing transform. This operator can extract the information that is most relevant to the time-varying characteristics of the signal in each small frequency band and retain it， which achieves the concentration again. The analysis of simulation signals verified the feasibility of the method. Finally， by analyzing the actual bearing signals， it is found that the proposed method is more effective than the previous time-frequency analysis methods.