Rolling element bearing fault diagnosis based on symptom parameter wave of acoustic emission signal

Due to the formation mechanism, AE technique has shown improved ability and performance on condition monitoring and fault diagnosis for rolling element bearing relative to vibration detection technique, especially on the detection of early defects. This article proposes a method of feature extraction applied on incipient fault AE signal of bearing. A method based on symptom parameter index and its derived mode according to information theory is presented to extract the fundamental information of fault such as the time and intensity of failure. Subsequently, a method compounding envelope analysis and power spectrum analysis dealing with symptom parameter index is proposed to discriminate fault patterns. Both simulated and experimental AE signals are used to verify the efficiency and accuracy of the proposed method. In conclusion it is shown that this detecting process can effectively extract fault feature and identify the fault types.