Study Of Acquiring Fault-diagnosis-rule For Mine Local Ventilation Facilities System

Mine local ventilation facilities system failure is the one of the key reasons of incidental gas explosions took in heading face. For mine local ventilation facilities system incorporates a number of components, involving more types and causes of the failure, resulting from mine staff cannot timely carry out accurate and rapid analysis of faults, extended the wind stopped time for the gas explosion accidents created conditions. Therefore, faulty diagnosis methods and rules of the mine local ventilation facilities system are researched to reduce the facilities maintenance time, to avoid disallowed gas concentration.(1)The fault decision-making information system for mine local ventilation facilities system is established based on rough sets, aimed at the fault of the whole facilities system. The method of attributes reduction and faulty diagnosis rule acquisition is analyzed in this paper. This method includes fixing on attributes collection and faulty samples, reduction based on discernibility matrix, and the process analysis of diagnosis rule acquisition according to rules compatibility.(2)The whole facilities system and a low voltage control switch is taken as examples for demonstration. The faulty diagnosis decision information system is founded through collection and dealing with fault data. The key fault attributes is definition by reduction information based on discernibility matrix. The diagnosis rules are obtained by combination and reduction. The rules conform to the coal mine actual faulty diagnosis process. The time of diagnosis common fault of system and difficulty of diagnosis are declined by using simple diagnosis rules.(3)The fault-diagnosis-rule acquisition system is researched in this paper, including analysis of whole system structure and realization of key technology. The decision-making information table and user interface are developed using Microsoft Access and VC++, as well as fault-diagnosis-rule database. This system can carry on the task for automatization and intellectualization for acquisition of rules.