国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Due to the long-term influence of dust from outside intrusion, it often happens faults in inner race or outer race of bearings in aviation logistics transmission equipment. That acquisition of vibration signals and extraction of the fault feature is a common method for bearing fault diagnosis. A fault feature extraction method for bearing based on sparse decomposition is proposed. Firstly, the mechanism of sparse extraction is analyzed. Then, a parameterized dictionary is predefined and Matching Pursuit (MP) optimized by Genetic Algorithm (GA) is utilized to extract the bearing fault feature, whose iterating termination condition is determined by the kurtosis of approximation signal. Finally, the feature component is reconstructed and the fault type can be determined according to its envelope spectrum. The test results of simulate data and bearing vibration data demonstrate that the proposed method can effectively extract the fault feature component and meets the demand of real-time bearing condition monitor in aviation logistics transmission equipment. 