A Neural Network Post-secondary System Can Be Used For Fault Diagnosis Of Locomotive Parts

The paper proposes a model to support fault diagnostic expert system for large and complex diesel locomotive. The model based on the structure and the parts of diesel locomotive is a distributed and cooperated expert system. The rules and knowledge is distributed many subordinate expert system. In the subordinate expert system, there are two ratiocinating modes. The one is the mode based on rules by usual expert system another is mode using neural network to recognize the type of the faults . That is giving the system the ability to recognize the fragmentary data or the data with noise. The system used the ART neural network because study of the ART is without teachers and can learn real time, So it is suited to recognize the faults that happen by accident.the system uses the advanced ratiocination such as uncertain ratiocinations and the default ratiocinations. By used the degree of reliability and the degree of importance to solve the confliction.The paper analysis the structure and makeup of diesel locomotive firstly, gives the the principle and realization of the expert system and gives the concrete design and implement of subordinate expert system, it evaluates the advantages and disadvantages of the system and give the area suited for this system and look forward the development of fault diagnosis system.