Research On Intelligent Fault Diagnosis Method Of Rolling Bearing Based On CYCBD And HRE

As a vulnerable part of rotating machinery,rolling bearing fault diagnosis and state identification have important practical significance.In the actual working condition,the collected bearing vibration signal usually contains a lot of noise,and the fault signal is coupled with other vibration signals in the transmission path,which makes the fault diagnosis more difficult.Besides,the nonlinear characteristics of the bearing vibration signal and the fluctuation of the amplitude of the same fault signal make it difficult to extract the fault features accurately,and the accuracy of fault identification is low.Aiming at the above problems,this paper takes rolling bearing as the research object,and studies the methods of bearing fault signal noise reduction,feature extraction and state recognition.The specific research contents are as follows:(1)Study the theory of maximum cyclic stationary blind deconvolution(CYCBD).Aiming at the problem that improper filter length and cycle frequency affect the effect of CYCBD,this paper proposes a signal denoising method based on adaptive CYCBD.Combined with the Teager energy demodulation method,a fault diagnosis method for artificially identifying faults is proposed.Firstly,the law of the pulse extraction effect of CYCBD changing with the filter length and cycle frequency is analyzed by simulation signal.Secondly,in order to determine the cycle frequency,this paper proposes a method of cycle frequency estimation based on morphological envelope autocorrelation function.Finally,in order to determine the length of the filter,the performance efficiency ratio index considering the filter effect and efficiency is proposed.Furthermore,the adaptive principle of the filter length is proposed by combining the equal step search strategy.Finally,the rationality of the proposed method is verified by simulation and experiment.(2)Aiming at the problem that the non-linear features of bearing fault signals and the fluctuation of the amplitude of the same fault signal make it difficult to extract the fault features accurately,this paper proposes a fault feature extraction method based on hierarchical range entropy.As a nonlinear analysis method,entropy can characterize the complexity of different faults.However,the commonly used entropy,such as fuzzy entropy,does not take into account the change in the entropy caused by the amplitude fluctuations,which causes the fault to be wrongly identified.Although the range entropy considers the amplitude fluctuation,it cannot analyze the signal from multiple scales.Based on the advantages of AHP,this paper puts forward the index of the entropy of the range of the hierarchy.The analysis results of measured signals show that the fault feature extracted by hierarchical range entropy is more effective than that of multi-scale range entropy which only considers low frequency components and hierarchical fuzzy entropy which does not consider amplitude fluctuation.(3)For the problem of low accuracy of fault identification,this paper proposes a fault identification method based on self-organizing fuzzy logic classifier(SOF).The SOF classifier has the advantages of high accuracy and high efficiency,and can be trained on the original basis after the new sample category is added,without restarting training,but the establishment of its structure involves the determination of the granularity level and the sample distance measurement method.In this paper,the measurement method of filter level and sample distance is determined through the analysis of the measured signal,and the intelligent identification method of bearing fault is proposed in combination with the LDA dimensionality reduction method.The experimental results show that: 1)Feature redundancy will reduce the classification accuracy,and LDA dimensionality reduction can improve the accuracy of fault recognition.2)Compared with the hierarchical fuzzy entropy without considering the amplitude fluctuation,the classification accuracy obtained by the hierarchical range entropy is higher.3)Noise will reduce the fault recognition rate,and CYCBD noise reduction can improve the fault recognition accuracy rate.