Application Of Acoustic Emission Technique In Fault Diagnostics Of Rolling Bearing

Rolling element bearings are of great importance to almost all forms of rotating machinery. Therefore, bearing failure is one of the foremost causes of breakdown in rotating equipments. As a consequence, the condition monitoring and fault diagnosis of rolling element bearings are very important. The application of acoustic emission (AE) in condition monitoring and fault diagnosis of rolling bearing is widely researched in present.This dissertation uses AE technique to perform theoretical and experimental study of AE based diagnosis method of the rolling bearing artificial pitting. The main contents include the following three aspects:1. Parameter analysis of rolling bearing AE signals. Concerning different operation conditions such as varying load, rotating speed, fault scale and propagation, parameters including ring down counts, amplitude, RMS and kurtosis are used to analyze the AE signals. And the varying trend and the sensitivity of these parameters to different operation conditions are concluded.2. Optimization of parameters selection of Morlet continuous wavelet transform (CWT). Concerning the selection of scales in CWT and waveform parameters of base wavelet, optimization method based on genetic algorithm (GA) is used to analyze the selection. The final optimization result can clearly reveal the time-frequency features of rolling bearing AE signals.3. Waveform analysis of rolling bearing AE signals. Based on the parameters optimization of CWT, an analyzing method based on time-frequency scalogram is proposed to process AE signals from different operation conditions and different propagation routes. With the help of Hilbert-Huang (HHT) transform, features of AE signals are extracted more clearly. Moreover, using the discrete wavelet transform (DWT), the quantitative analysis is applied to AE signals.