Research on Fault Diagnosis Expert System Fusing the Neural Network Knowledge
For a complicated system based on high technology, once a part breaks down, the entire system can not work normally. Moreover, due to the complexity of its structure and fault causes, the fault diagnosis of the system is also complex and indeterminate, a single test equipment can hardly finish a difficult diagnose task, and fault diagnosis expert system can resolve these problems effectively. The traditional diagnose expert systems have many problems such as the bottleneck of knowledge acquisition, the fragility of knowledge, the pool ability of selfstudy, the inefficient reasoning, and the monotoniciry of reasoning, so there are certain limitations. But the artifical neural networks technology is a new system, it is an mathematical model that applies the structure like the joint of synapses in hypothalamic neurons, which has the strong ability to study, and can learn from samples, obtain knowledge, store it in the network in the form of weight and threshold; and it is easy to implement the parallel processing, has the character of association memory, own the better robust.it ability of adaptive self-study is manifested mainly in adjusting the weight of network according to the change of enviroment by learning algorithms, so as to adapt to the environmental change. But the neural network can not explain its own reasoning. Therefore we will apply the neural network to the expert knowledge system, which can make them learn each others good points mutually for common progress, constructing the new neural network expert system. The system is applied to the power fault diagnosis, achieving good results.
expert system neural network radar power system
Yingying Wang Ming Chang Hongwei Chen Yueou Ren Qiuju Li
Changchun Institute of Engineering Technology.Changchun.China.130117
国际会议
杭州
英文
196-200
2011-08-26（万方平台首次上网日期，不代表论文的发表时间）