Mechanical Fault Diagnosis and Signal Feature Extraction Based on Fuzzy Neural Network
会议名称：《第二十七届中国控制会议》
会议日期：2008年
学科分类：12[管理学] 07[理学] 08[工学] 0711[理学-系统科学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080202[工学-机械电子工程] 081104[工学-模式识别与智能系统] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by National Nature Science Foundation underGrant 59775004
关 键 词：Wavelet transform Fuzzy theory Fault diagnosis Signal de-noising Aeroengine
摘 要：To improve the limitation of applying traditional fault diagnosis method to the diagnosis of multi-concurrent vibrant faults of aeroengine,a new diagnosis approach combining the wavelet transform with fuzzy theory is proposed.A novel method based on the statistic rule is brought forward to determine the threshold of each order of wavelet space and the decomposition level adaptively,increasing the *** effective eigenvectors are acquired by binary discrete wavelet transform and the fault modes are classified by fuzzy diagnosis equation based on correlation *** fault diagnosis model of aeroengine is established and the extended Kalman filter(EKF) algorithm is used to fulfill the network structure and the robustness of fault diagnosis equation is *** means of choosing enough samples to train the fault diagnosis equation and the information representing the faults is input into the trained diagnosis equation,and according to the output result the type of fault can be *** applications show that the proposed method can effectively diagnose multi-concurrent fault for aeroengine vibration and the diagnosis result is correct.