A new model of mine hoist fault diagnosis based on the rough set theory

Extraction of simple and effective rules for fault diagnosis is one of the most important issues needed to be addressed in fault diagnosis, because available information is often inconsistent and redundant. This paper presents a fault diagnosis model based on rough set theory. Firstly, this model can discretize fault continued attributes using a modified genetic algorithm. Then, reduce diagnosis rule by using heuristic algorithm of rough set theory, a set of diagnosis rules are generated and a rule database for fault diagnosis is established. Simulation results for fault diagnosis of mine hoist show that this method improves the accuracy rate of fault diagnosis, predigest the number of feature parameters and diagnostic rules, and reduces the cost of diagnosis, with more applicable than the classical RS-method in practical applications.