Research About Diagnosis For Mine Local Ventilation Facilities Based On Rough Sets

Due to heavy coal exploitation in recent years, many serious accidents caused by gas explosion in coal mines frequently happened, which jeopardized to people's lives and wealth. Mine local ventilation facilities failure is one of the main reason which leads to the accident of gas explosions in heading face. The mine local ventilation facilities incorporates a number of components, involving many types and reasons of the failure. The results that mine staff cannot timely carry out the problem, extended the wind stopped time and increased the probability of the gas explosion.Rough set theory, as a new academic hot spot in artifitial intelligence, without any prior information, can efficiently analyze and dispose of imprecise, inconsistent, incomplete data. It reveals the hidden laws by the discovery of nexus between the data, in order to pick up the useful information and simplify the information disposal. Based on the rough set theory, this paper carries out research on the rules of the failure diagnosis in partial mine ventilation facilities. It instructs how to estimate the cause of the failure quickly and exactly, shorten the time of stopping the wind, and reduce the probability of the gas explosion.Firstly, in this paper, rough sets are elaborated. Attribute reduction algorithms based on rough set theory are analyzed. Secondly, aim at the problem that the frequency property of the algorithm based on discernibility matrix may not be able to find the right attribute reduction, and the factor that the decision table whether or not compatible, the frequency property of the algorithm based on Discernibility matrix proposed are be improved, to avoid the blindness of the calculation, so that more accurately reducted results are obtained. The effectiveness of the algorithms in this paper is clearly demonstrated by the experiment results. Finally, according to the fault of the whole facilities, the fault decision-making information system for mine local ventilation facilities is established based on rough sets. Adopt the improved algorithm in this paper, carrying out attribute reduction and value reduction. This paper determined the key fault attributes and accessed the fault diagnosis rules. Analysis shows that the time and difficulty of diagnosis are declined by using the method based on the rough sets.