Fault detection method based on computational intelligence technology fusion
Chen Wu · Journal of Zhejiang University(Engineering Science) · 2010
A computational intelligence technology fusion method of fault detection was proposed in order to improve the detection precision and decrease the misinformation detection of complex system.The various approaches such as rough set,genetic algorithm and neural network were integrated to synthesize their merits for fault detection.According to the uncertainty and imperfection of the original sample data,the rough set was used to pretreat for the normalization of data,the discretization of continuous data and the attribute reduction in order to obtain the minimum fault feature subset.The genetic algorithm with the ability of strong global search was used to train the weights of back propagation neural network.The minimum reduced subset was inputted into the trained network to construct the fault detection model that can classify the pretreated fault feature vectors under certain states to realize the fault detection.The motor bearing experiment results show that the method can optimize the structure of neural network and improve the rate and precision of fault detection.