A Fuzzy Inference System for Gearbox Failure Diagnosis

A Fuzzy Inference System for Gearbox Failure Diagnosis

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. To determine the condition of an inaccessible gear in an operating machine the vibration signal of the machine can be continuously monitored by placing a sensor close to the source of the vibrations. These signals can be further processed to extract the features and identify the status of the machine. This paper presents the use of decision tree for selecting best statistical features that will discriminate the fault conditions of the gearbox from the signals extracted. The features of WT values of vibration signal were extracted using descriptive statistical parameters. J٤٨ algorithm is used as a feature selection procedure to select pertinent features from data set. The output of J٤٨ algorithm was employed to produce the crisp if-then rule and membership function sets. A fuzzy classifier is built and tested with representative data. After a test under normal condition, a number of different machine defect conditions were introduced for one working level of gearbox speed (١٥٠٠ rpm), corresponding to (i) Broken Gear (ii) Worn Gear, and (iii) Faulty Baring. The structure of FIS classifier was then defined based on the crisp sets. In order to evaluate the proposed WT-J٤٨-FIS model, the data sets obtained from vibration signals of the gearbox were used. Results showed that the total classification accuracy was ٩٧.١٤%.