Bearing Fault Diagnosis Based On Wavelet Packet And Multiple Classifiers Group Of FCM

The rolling bearing is the most widely used mechanical components in a variety ofrotating machinery, its condition monitoring and fault diagnosis possess significance. Inorder to improve the accuracy of the bearing fault diagnosis system, bearing fault featureextraction based on wavelet packet and fault recognition based on FCM classifier group.First of all, the analysis of the failure mechanism of rolling bearing, time domain andfrequency domain based on Fourier transform are used in the bearing vibration signalfeature extraction and analysis. The analysis showed that the failure characteristics of thesignal based on time domain and Fourier transform have their own limitations.Furthermore, using the signal energy feature extraction methods based on wavelet packet.Secondly, for the robustness, multiple classifier group is able to overcome the falsepositives caused by data distortion or missing due to some reason. Using multipleclassifiers of FCM achieve the classification of fault characteristics in the faultidentification. FCM algorithm has better search capabilities which is a local searchalgorithm and so sensitive to the initial value of the cluster center that is easy to fall intolocal minima. In order to avoid the local optimum, using Particle Swarm Optimizationalgorithm, which of the global search ability optimize the cluster centers of FCM andachieve the failure feature recognition method of FCM classifier group based on PSO.Finally, in order to improve the classification accuracy rate of multiple classifiersfusion system and the robustness of the system, fuzzy integral fusion method is usedduring the FCM classifier group fusion. Based on fuzzy integral classifier fusion system,fuzzy measure has a great impact on the performance of the fusion system and is given onthe pre-anthropogenic. Therefore, the PSO algorithm is used to optimize the fuzzymeasure and improve the classification accuracy and efficiency based on fuzzy integralfusion group. Simulation studies show that the fault diagnosis method based on waveletpacket and FCM classifier group is effectively improve the recognition accuracy.