摘  要：針對滾動軸承故障振動信號的非平穩性特點，提出一種改進經驗模態分解（EMD）和形態濾波相結合來提取故障特征信息的方法。該方法首先在原信號中加入高頻諧波并進行EMD分解，減小傳統EMD分解中存在的模態混疊現象，然后從高頻本征模態分量（IMF）中去除高頻諧波得到故障沖擊成分，經形態濾波消噪后進行頻譜分析，提取出故障特征信息。信號仿真分析該方法的實施過程，并將該方法成功運用于滾動軸承內圈和外圈故障的診斷。實驗結果表明該方法能夠有效提取滾動軸承故障特征信息，實現故障診斷。
關鍵詞：改進經驗模態分解；形態濾波；滾動軸承；故障診斷
文獻標志碼：A       文章編號：1674-5124（2016）01-0121-05
Rolling bearing fault diagnosis based on improved EMD and morphological filter
WEN Cheng， ZHOU Chuande
（College of Mechanical and Power Engineering，Chongqing University of Science and Technology，
Chongqing 401331，China）
Abstract: A new technology is proposed to solve the non-stationarity in vibration signals of antifriction bearing faults in accordance with the improved empirical mode decomposition （EMD） and morphological filters. First， a high-frequency harmonic was added into the original signal and then decomposed by means of EMD to reduce the mode mixing phenomenon in traditional EMD. Next， the high-frequency harmonic was removed from the high-frequency intrinsic mode component （IMF） to obtain fault impact compositions. The fault characteristic information was extracted by spectrum analysis after morphological filter de-noising. At the same time， the above steps were simulated by signals. This method was applied to diagnose the faults in inner and outer races of antifriction bearings. The experimental results show that the method can extract the fault characteristics and diagnose the faults of antifriction bearings.
Keywords: improved empirical mode decomposition； morphological filter； rolling bearing； fault diagnosis