Rotor unbalance fault diagnosis using DBN based on multi-source heterogeneous information fusion

Abstract In the age of Internet of Things and Industrial 4.0, new advanced methods need to be proposed to analyse massive multi-source heterogeneous data from rotating machinery since traditional data analysis methods are difficult to mine features effectively and provide accurate fault results automatically. This paper proposes a rotor unbalance fault diagnosis method using deep belief network (DBN) to learn the representative features automatically and accurately identify fault states. Multi-source heterogeneous information composed with vibration signal and shaft orbit plots generated by raw displacement signals can fully exploit multi-sensor information in fault diagnosis. And multi-DBN model was introduced to deal with multi-source heterogeneous information fusion problem containing all fault information which could adaptively learn useful features through multiple nonlinear transformations compared with traditional approaches depending on time-consuming and labour-intensive manual feature extraction. The results indicate that the accuracy of classifying rotor unbalance fault states is up to 100% under proper parameters of DBN which significantly improves the effect of fault recognition and validates effectiveness using the proposed method.