Fault Diagnosis of Machine Tool Based on Rough Set
Xie Nan, Xue Wei, Xinfang Liu · Research Journal of Applied Sciences Engineering and Technology · 2013
Fault diagnosis of machine tool plays an important role on advanced manufacturing. The correct and rapid identification of faults depends on diverse sensing data and reasonable knowledge intensively. In this paper, a method based rough set is applied to diagnose faults of machine tool during the processing and a rapid fault diagnosis system based on rough set is also proposed. The approach correctly extracts diagnosis knowledge from the data that are obtained from both sensors and inspection devices and then generates a set of minimal diagnostic rules which could be used to quickly determine the failures of mechanical process, combined with data. Furthermore an actual instance is presented to illustrate the efficiency of the method in the end.