Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning

Abstract: Roller bearing is one of the most widely used elements in rotary machines. Condition monitoring of such elements is conceived as pattern recognition problem. Pattern recognition has three main phases: feature extraction, feature selection and feature classification. Histogram features can be used for fault diagnosis of roller bearing. This paper presents the use of decision tree for selecting best few histogram features (bin ranges) that will discriminate the fault conditions of the bearing from given train samples. These features are extracted from vibration signals. A rule set is formed from the extracted features and fed to a fuzzy classifier. The rule set necessary for building the fuzzy classifier is obtained largely by intuition and domain knowledge. This paper also presents the usage of decision tree to generate the rules automatically from the feature set. The vibration signal from a piezoelectric transducer is captured for the following conditions – good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race fault. The histogram features were extracted and good features that discriminate the different fault conditions of the bearing were selected using decision tree. The rule set for fuzzy classifier is obtained by once using the decision tree again. A fuzzy classifier is built and tested with representative data. The results are found to be encouraging. [ABSTRACT FROM AUTHOR]