Abstract
Abstract
Gearbox is an inseparable part of any rotating machinery today. Gearbox transfers speed and torque from one shaft to another. Hence, correct diagnosis of gearbox faults is an important and critical task for maintenance operators. But, due to nonlinear, time-varying behavior and imprecise measurement information of the systems it is difficult to deal with gearbox failures with precise mathematical equations. Human operators with the aid of their practical experience can handle these complex situations, with only a set of imprecise linguistic if-then rules and imprecise system state. The purpose of this study is to provide a correct and timely diagnosis of gearbox failures in the context of condition based maintenance. The diagnosis is performed by knowledge acquisition through a fuzzy rule-based inference system which could approximate human reasoning. The proposed approach is tested and applied to an experimental data emanating from a gearbox system.
Get full access to this article
View all access options for this article.