Research Of Fault Diagnosis To Control System Based On Neural Network

With development of technology, control system is more and more complicated. So it becomes more and more importance, diagnosing accurately fault in time and ensuring that system runs reliably. The paper introducing the tasks of fault diagnosis, discuss the fault in the control system and their mathematical representing methods.In the control system of neural network, the fault diagnosis consists two parts: one is the collection of fault sample data, the other is fault diagnosis. So during the collection of fault sample data and checking data, the paper sets up the mathematics model of control system first. We cause all kinds of fault in the control system, then collects a variety of fault data. After unitarily dealing, the fault data server as the sample data of training neural network. At the same time, according to the collection of checking data, it examines whether the trained neural network have the function of fault diagnosis. In the process of diagnosis, as the response of the slow constringency rate and disadvantage that error easily gets into partial minimal value of BP algorithm in using BP network. It adopts three solutions. First, using the learning method of "batch dealing". During the training neural network, this method accelerates the speed of constringency, neglecting the effect of studying sample order. Second, adopting the improving BP arithmetic, Conjugated Gradient Method. It adds the inertia in the process of iterative. Third, using the learning rate adaption, it improves the speed of constringency and accurate rate of diagnosis fault.By using the method of this paper, we collect the data of a pump-jack electric current and diagnose it faults. The result proves, the diagnosis method on fault by neural network can be used in reality.