An intelligent model based on data mining and fuzzy logic for fault diagnosis of external gear hydraulic pumps

Abstract

This paper presents a fault diagnosis method based on a fuzzy inference system (FIS) in combination with decision trees. Experiments were conducted on an external gear hydraulic pump. The vibration signal from a piezoelectric transducer is captured for the following conditions: ‘Normal pump’ (GOOD), ‘Journal-bearing with inner face wear’ (BIFW), ‘Gear with tooth face wear’ (GTFW) and ‘Journalbearing with inner face wear and Gear with tooth face wear’ (G&BW), for three working levels of pump speed (1000, 1500 and 2000 r/min). The features of signal were extracted using descriptive statistic parameters. The J48 algorithm is used as a feature selection procedure to select pertinent features from the data set. The output of the J48 algorithm is a decision tree that was employed to produce the crisp if-then rule and membership function sets. The structure of the FIS classifier was then defined based on the crisp sets. In order to evaluate the proposed J48-FIS model, the data sets obtained from vibration signals of the pump were used. Results showed that the total classification accuracy for 1000, 1500 and 2000 r/min conditions were 100%, 96.42% and 89.28%, respectively. The results indicate that the combined J48-FIS model has the potential for fault diagnosis of hydraulic pumps.