Rule Acquisition Method of Hoist Fault based on Rough Set
Niu Qiang, J Xuzhou · Microcomputer Information · 2008
Extraction of simple and effective decision rules for fault diagnosis is one of the most important issues needed to be ad- dressed in mine hoist, because available information is often inconsistent and redundant. This paper presents a fault diagnosis model based on rough set theory. From original fault data containing inconsistent and redundant information, a set of maximal generalized decision rules with certainty factor and coverage factor are generated by using a proposed value reduction algorithm, and therefore a decision rules base for fault diagnosis is established. Simulation results for mine hoist show that the method improves the rate of fault diagnosis, decreases the number of feature parameters and diagnostic rules, and reduces the cost of diagnosis.