Research On Fault Diagnosis For Rolling Bearing Based On Variational Mode Decomposition

Rolling element bearing is one of the most commonly used components in the rotating machinery, thus it is extremely significant to research on the condition monitoring and fault diagnosis methods of rolling element bearings. Early diagnosis of bearing failure is significant that it can be effective in preventing the occurrence of bearing failure, and ensure the safe operation of the mechanical system. The rolling element bearing is taken as the research object in this paper. Aiming at the key issue of the bearing fault diagnosis, viz., feature extraction, a series of research work is conducted by using advanced signal processing techniques. The main contents are listed as follows:Firstly, from the viewpoint of theoretical analysis and engineering application, the background and significance of the present study are elucidated. Provide a more comprehensive elaboration of rolling bearings, which include the structure、vibration mechanism、diagnostic methods and the application of diagnostic methods. Then the calculation of each part fault characteristic frequency in rolling bearing was elaborated based on the mechanical fault diagnosis theory.Secondly, in order to overcome mode mixing and under envelope of empirical mode decomposition, the variational mode decomposition(VMD) was introduced from tones separation, over- and underbinning, sensitivity to initialization and non-stationary multimode signals. The method was applied to simulated signals, and the results show that VMD can separate frequency completely for non-stationary signals.Thirdly, in order to solve the problems that the fault feature of rolling bearing in early failure period is difficult to extract, an incipient fault diagnosis method for rolling bearing based on variational mode decomposition(VMD) and Teager energy operator was proposed. Firstly, VMD was used to decompose the fault signal into several intrinsic mode functions(IMFs), and then the IMF of the two biggest kurtosis were selected with Kurtosis Criterion and demodulated into Teager energy spectrum with Teager energy operator. The proposed method was applied to simulated signals and actual signals. The results show that this method improves the efficiency of signal decomposition and reduces the effect of noise, enabling accurate diagnosis of rolling bearing fault, the analysis results demonstrated the effectiveness of the proposed method.Finally, according to the non-stationary feature of the vibration signals from rolling bearing and the situation it was hard to obtain enough fault samples, thus a fault diagnosis method for rolling bearing based on variational mode decomposition(VMD) permutation entropy and SVM was proposed. Firstly, VMD was used to decompose the fault signal into several intrinsic mode functions(IMFs), and calculate the IMF’s permutation entropy and then combine every entropy to get the feature vector, finally keep the vector as the input of SVM, the classification result will be given by the SVM. The proposed method was applied to actual signals of rolling bearing. The results show that this method enables accurate diagnosis of rolling bearing fault, and the analysis results demonstrated the effectiveness and feasibility of the proposed method.