Research On Fault Diagnosis Method Of Rolling Element Bearings Base On Full Vector CYCBD

With the progress and development of modern industry,the application of rotating machinery is becoming more and more extensive.As an important part and vulnerable part of rotating machinery,rolling bearing will have a great impact on the safety and stability of equipment once it breaks down.Therefore,timely and effective research on fault diagnosis and pattern recognition have important practical significance.In this thesis,the rolling bearing is taken as the research object.The full vector spectrum technique and the improved and optimized Maximum Second-order Cyclostationarity Blind Deconvolution(CYCBD)algorithm are used to preprocess the bearing fault data and extract the relevant fault features.At the same time,the Variable Predictive Model Based Class Discriminate(VPMCD)is used to recognize the fault pattern.The main research contents are as follows:In view of the non-stationary and nonlinear characteristics of the fault signal,and to solve the problem that the cyclic frequency parameters need to be set in advance when the CYCBD is filtered,this thesis introduces Adaptive Local Iterative Filtering(ALIF)to preprocess the fault signal,and proposes a method for extracting the fault features of rolling bearings based on ALIF-CYCBD.The source signal is filtered and denoised by ALIF algorithm.The correlation component is selected by joint coefficient criterion for reconstruction,and the parameters needed by CYCBD algorithm for signal decomposition are set by analyzing the estimated value of the reconstructed signal.Finally,the reconstructed signal is processed and analyzed by CYCBD algorithm.In this thesis,the algorithm theory and numerical calculation process of ALIF and CYCBD are introduced,and the vibration simulation signal analysis and rolling bearing fault signal experiment verification are carried out.At the same time,comparative analysis is added to verify the effectiveness of the proposed method in extracting bearing fault features.The further analysis of CYCBD algorithm parameters shows that the prior setting of filter parameters also has a certain influence on the decomposition effect of CYCBD.Therefore,in order to solve the problem that CYCBD algorithm can adaptively select the optimal parameters,this thesis introduces the Improved Whale Optimization Algorithm(IWOA)based on nonlinear convergence to optimize the filter length and cyclic frequency,and takes the cyclic frequency estimated by ALIF as the optimization range of this parameter.Because the traditional signal analysis based on single channel is easy to cause the omission of fault feature information,this thesis introduces the Full Vector(FV)spectrum technology to realize the data level fusion of the signals of the same source and two channels,so as to realize the comprehensive description of the bearing fault signals.To sum up,this thesis puts forward a fault feature extraction method for rolling bearings,which combines the full vector spectrum technology with the optimized CYCBD algorithm.After introducing the full vector spectrum theory and the algorithm flow,the experimental verification of the same two-channel bearing data signals proves that the proposed method has a better effect in suppressing noise and improving signal-to-noise ratio,and has a more comprehensive ability to extract bearing fault information compared with single-channel signal analysis.The essence of mechanical fault diagnosis is the process of pattern recognition.Therefore,in this thesis,the pattern recognition of bearing fault state is realized by combining FV-CYCBD with VPMCD.Firstly,the FV-CYCBD method is used to extract the features of the same two-channel signals after FV fusion and form a feature vector group,which is divided into a training group and a test group.The training group samples are input into the VPM prediction model for training and learning,and the pattern recognition is verified by the testing group samples to realize the pattern recognition process of rolling bearings.