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

► The paper presents the result of fuzzy classifier with histogram features for fault diagnosis. ► The study focuses on automatic rule learning using decision tree C4.5 algorithm. ► The case study is bearing fault diagnosis using histogram features of vibration signals. ► The classification accuracy is found to be 97%.