Application And Research On Fault Diagnosis Of Rolling Bearing Based On AMD And EMD Method

Rotating machinery plays a very important role in industrial production, and the rolling bearing is most widely used in rotating machinery, its running state will affect the performance of the mechanical equipment. Therefore, the rolling bearing fault diagnosis has important practical significance. In this paper, the limitations and shortcomings of the EMD method are analyzed and studied, combined with the AMD method and bistable stochastic resonance theory to improve it. The proposed new algorithm is more conducive for the extraction of faults, and successfully applied to the fault diagnosis of rolling bearing.A rotating machinery fault feature extraction method based on AMD is proposed in this paper. For the fault feature frequency can be predicted in rotating machinery fault diagnosis, we can use AMD method to extract fault feature frequency signal in mechanical vibration signal and get its frequency spectrum. If the frequency spectrum contains the fault feature frequency, it shows that the faults exist in mechanical vibration signal. The analysis of the rolling bearing fault signal and the comparison with EMD, it show that the AMD method is effective and more rapid, accurate than EMD.Aiming at mode mixing of EMD caused by the intermittency signal,a new method to eliminate mode mixing of EMD based on analytical mode decomposition, AMD is proposed. In this method, taking advantage of instantaneous frequency characteristics of the first intrinsic mode function, IMF, we can get the frequency components of IMF1, the bisecting frequency and the location of the intermittency signal. Then, using AMD to extract the intermittency signal and decompose the processed signal by EMD method.Thus, the effect of the intermittency signal is eliminated. The results of simulation analysis and engineering application show that the proposed method can effectively eliminate mode mixing of EMD caused by intermittency signal.Aiming at the method of Hilbert-Huang Transformation unable to distinguish a signal with closely spaced frequency components, a new frequency detection method of non-stationary signal based on AMD and HHT is proposed. Get the frequency componentsof the signal by HHT firstly. Then, using AMD to extract the signal of different frequency components and decompose the signal, and judge whether the frequency components contains more than a frequency components. If there are two or more frequency components and separated by AMD method. The results of simulation and engineering application show that the method can solve the problem of HHT cannot effectively separate a signal with closely spaced frequency components. This method ensures the correct and effective data signal decomposition and improves the accuracy of signal decomposition.An adaptive stochastic resonance and AMD-EEMD method is proposed for fault diagnosis of rolling bearing. Firstly, the stochastic resonance system is optimized by particle swarm optimization(PSO), and the best structure parameters are obtained. Then,the signal with noise is put into the stochastic resonance system and denoising and enhancing the signal. Secondly, the signal output from the stochastic resonance system is extracted by AMD method. Finally, the signal is decomposed by ensemble empirical mode decomposition EEMD method. The simulation results show that the optimal stochastic resonance system can effectively improve the signal to noise ratio, the effective components of EEMD decomposition is significantly reduced after using AMD, thus improving the decomposition results of EEMD and enhancing the amplitude of components frequency. Through the extraction of the rolling bearing fault signal feature proved that the method has a good effect.Finally, a rolling bearing fault diagnosis system is designed based on the Lab VIEW software development platform, including time domain analysis, frequency spectrum analysis, AMD decomposition and EMD decomposition, etc. And each module is introduced. Through the analysis of a bearing fault signal, the fault diagnosis system is proved to be able to realize the fault diagnosis and identification.