Knowledge discovery for gearbox fault diagnosis using flow graph

Abstract

It is difficult to discover gearbox diagnosis knowledge while diagnosis information is incomplete. To overcome this problem, a novel knowledge discovery method for gearbox fault diagnosis using flow graph (FG) is presented. In this method, FG is constructed in terms of incomplete fault decision table. The relationship among fault attributes can be represented in a graphical manner. Assignment reduction algorithm is used to remove irrelevant and redundant nodes. Therefore, FG after reduction is acquired according to the minimal reducts. To validate the performance of this method, a gearbox fault diagnosis experiment was performed. The experimental studies indicate the proposed method can be utilized to directly discover gearbox diagnosis knowledge from incomplete information in a graphical and intuitive manner.