The Research Of Fault Diagnosis Method Based On Rough Sets And Neural Network

With the development and appliment of computer and control technical, the automation of system devices has being elevated sharply. The reason,process and phenomenon of fault is complicated. Some theory in computational intelligence fields, such as artificial neural network, Rough Set theory has been widely used in fault diagnosis. Rough Set theory is a tool for fuzzy and dubious knowledges. It can simplify decision rules and find useful information. It also can settle the problem caused by redundant data. But the fault-tolerance ability of rough sets is insufficiently ideal. When core's attributes are polluted by noise, it may arouse error judgment. Therefore in order to enhance the fault-tolerance ability of Rough sets theory, unify Rough sets and neural network, construct an intelligent mixture system of Rough sets and neural networks, which fully develops the reduction ability of Rough sets and the classification ability of neural networks. In this paper, combining RS and ANN, using the both advantages, giving a fault diagnosis system model based on the rough set-neural network.Firstly, studied the attributes reduction methods used in rough set theory. The knowledge reduction is the core part of Rough sets, but it is a NP(Non-deterministic Polynomical) problem on theory. And on this basis giving a method based on the improved binary matrix and boolean algebra for attribute reduction, reduce the amount of computation, the request of attribute reduction can be quickly given.Secondly, researching the discrete method. Rough set theory is based on discrete data processing methods, discretization of continuous data directly impact on its effect. In this paper given a method based on SOM(Self-Organizing feature Map), transformated the input vector values which are detached from each other into a new input mode. The results of example demonstrated the effectiveness of this method.Finally, we used the fault diagnosis system by rough-neural network on Tennessee-Eastman process(TEP) fault diagnosis, it achieved good diagnosis result.