TFC-TFECR feature extraction and state recognition of acoustic emission signal of cylindrical roller bearing

Rolling bearings are widely used in rotating machinery, such as aero-engine spindles, flying machines, wind turbines, etc. Bearing condition monitoring is of practical importance. The acoustic emission (AE) signal has impact and rapid attenuation characteristics. Most existing research on fault diagnosis is not focused on rapid attenuation characteristics. According to this characteristic, a time-frequency coherent and time-frequency energy change rate (TFC-TFECR) method is proposed to identify the AE signals of bearing faults. This paper investigates the effect of the time-frequency coherent (TFC) method on attenuation coefficient. It also focuses on the deviation of the time-frequency energy change rate of the TFC-TFECR method, which is superior to the time-frequency energy. Feature extraction of AE signals from cylindrical roller bearings is carried out through three typical states of cylindrical roller bearings. The feature values of the TFC-TFECR method are input into the SVM model, and the sparrow search algorithm optimises the SVM model. The experimental results show that the method can effectively realise the feature extraction and state recognition of the AE signals of cylindrical roller bearings, and the accuracy rate reaches 99.3827% at 600 r/min and 98.7654% at 1200 r/min. This paper provides a new method for non-destructive testing of rotating machinery bearings.