Fault diagnosis of elevator control system based on information fusion
基于信息融合技术的电梯控制系统故障诊断*

An method which based on information fusion fault diagnosis is proposed for the elevator control system because of its difficult and low diagnosis rate, while optimizing the network learning rate and slopes of activation functions that improve on the Back-propagation (BP) neural network and then make the network have more capable of nonlinear classification and higher convergent efficiency. Meanwhile, elevator control signals and running parameters are regard as the characteristics of neural network classifier, and then apply the Dempster-Shafer (D-S) evidence theory fusing the result of multiple classifiers. Through making the basic reliability distribution of the evidence theory not completely depending on the expert subjective valuations, the impersonal valuation is realized. The method has high stability and accuracy. Finally, experimental results show the effectiveness of the algorithm.