Vibration Fault Diagnosis of Mine Ventilator Based on Intelligent Method
Based on the analysis of the vibration fault features of mine ventilator, the paper established a fuzzy wavelet neural network model which can diagnose the faults of mine ventilator. The fuzzy wavelet neural network model unify fuzzy logic and BP neural network, using wavelet basis function as membership function. Furthermore, a hybrid learning algorithm based on self organized and supervised learning is also proposed. Through training the displacement factors, the dilation factors of wavelet basis function and the connection weight values of fuzzy neural network, the parameters and the structure of the network approximate to global optimization. The experiment results show that it not only raised the efficiency and accuracy of fault diagnosis, but also provide a valid approach to protect the safety of mine ventilator by using this intelligent method.
Fault Diagnosis Mine Ventilator Fuzzy Wavelet Neural Network
Ming Li BaoRan An Lei Yu
School of Information and Electrical Engineering, China University of Mining and Technology,Xuzhou, Jiangsu 221008
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
194-198
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）