Fault diagnosis based on deep belief networks and fisher discriminant analysis

With the development of data mining technology, it becomes vital to extract fault features effectively in fault diagnosis. This paper is mainly concerned about a hybrid method for fault diagnosis. Firstly, train the data with the deep belief networks (DBN) to get the weighting vectors for all the fault class. And then, weight for fault data with responding weight vector to obtain the summed data. Finally, use Fisher Discriminant Analysis (FDA) to do on the summed data sets, in order to diagnose the fault. Experimental results show the effective performance of this method with the Tennessee Eastman Process (TEP) benchmark.