Fault diagnosis of rolling bearing based on AR model of acoustic emission
Rolling bearing is one of the most widely used parts in rotating machinery.The fault diagnosis method and condition monitoring technology of rolling bearing is one of the key technologies to ensure the safe and stable operation of the machine.Using power spectrum analysis,classical spectrum estimation and other methods,the fault feature information is extracted,and the bearing state analysis based on vibration acceleration signal is com-pleted.However,due to the influence of vibration and high noise environment,it is difficult to complete the iden-tification of bearing early fault damage characteristics.In this paper,a sensing method with high frequency and high sensitivity is proposed.Compared with vibration detection technology,it has higher signal-to-noise ratio and can sense the impact response information of small size bearing early fault damage.Meanwhile,the Auto-Re-gressive AR model method and the power spectrum estimation method are combined to realize the identification of bearing early fault characteristics.In addition,taking the high-speed bearing of natural gas compressor as an ex-ample,the acoustic emission sensing technology with high frequency and high sensitivity is used to realize the fault feature state identification and analysis.The results show that the method is better than the classical spec-trum estimation method.It provides feasible theoretical support for the condition monitoring and correction of bearing fault damage generation,evolution to fault,and further ensure the safety of equipment operation.
acoustic emissionrolling bearingAR modelfault signal
万方数据
维普
