Rolling Bearing Fault Diagnosis Based On Masking Empirical Mode Decomposition And Phase Space Reconstruction Of Conditional Entroy

In mechanical equipment, rolling bearing is one of the most widely used basiccomponent, which operation in good condition or not often has a directly impact on theperformance of the whole machine. With the development of modern production and theprogress of technology, the stable operation of rolling bearing is receiving more and moreattention. Therefore, condition monitoring and fault diagnosis of rolling bearing has greatsignificance. In this paper, vibration signals of rolling bearing are taken as the researchmodel. According to the key problem of fault feature extraction, a bearing fault diagnosismethod based on chaos phase space reconstruction and masking empirical modedecomposition and cross approximate entropy is put forward.The main works of this paper are as follows:(1) The common fault types of mechanical equipment rolling bearings aresummarized as well as structure and vibration mechanism. Aiming at the limitations ofpast signal analysis methods, the research methods of this paper is established based onthe analysis of research status at home and abroad.(2) The basic theory and application of empirical mode decomposition method (EMD)is elaborated. The mode aliasing problems of empirical mode decomposition influencesseriously decomposition precision and effect. So the masking empirical modedecomposition method (MEMD) is introduced. This method retains the goodcharacteristics of EMD method and can obtain accurately the local signal characteristics ofdifferent scales, and also suppresses effectively the mode aliasing of EMD.(3) Compared with the limitations of current phase space reconstruction method, amethod based on condition entropy is analyzed, which can determine the delay time andembedding dimension simultaneously, so amount of calculation is reduced greatly. Aseries of simulation experiments verify the accuracy and efficiency of this reconstructionmethod.(4) Due to the complex non-linear and non-stationary characteristics of fault signal,cross approximate entropy (cApEn) is introduced on the basis of the phase space reconstruction, which can be used to characterize the irregular and complexity of signalsquantitatively. Cross approximate entropy method is the improvement of approximateentropy, which can reflect more dynamic characteristics of the signal. The fault signals aredecomposed by MEMD to obtain series of intrinsic mode function (IMF). Then the cApEnvalue of IMF component is calculated based on the phase space reconstruction, whichcompletes the extraction of fault feature. The fault feature vector is analyzed by fuzzyc-means (FCM) clustering and can achieve diagnosis and identification of rolling bearingfault.(5) The datasets of the rolling bearing fault from the Case Western ReserveUniversity is taken as the experiment research object, and the experiment is conductedfrom different damage degree and different fault part of rolling bearing. The resultsdemonstrate that the fault feature can be extracted effectively by cross approximateentropy based on the combination of phase space reconstruction and MEMD. And FCMclustering algorithm has also made good recognition effect in fault diagnosis.