Intelligent Fault Diagnosis of a Reciprocating Compressor Using Mode Isolation Convolutional Deep Belief Networks

Abstract

Due to the complex transfer paths of vibration signals, and a large number of vibration excitations, fault diagnosis of reciprocating compressors (RCs) has become one of the most challenging problems in the field of health monitoring. Focusing on fault diagnosis, a novel method, which will be referred to in this paper as mode isolation convolutional deep belief network (MI-CDBN), is proposed from the perspective of transfer path analysis, and multimodal data isolation. First, sparse filtering is applied to compress vibration signals and to reduce the computing cost. Second, the MI-CDBN is used to isolate multimodal data of different transfer paths and to calculate features using unsupervised learning. Finally, a multiclass logistic regression is employed to identify the fault types of the RC. Vibration signals from practical industries are used to validate the proposed method. The obtained results demonstrate that the proposed method has an improved performance compared to many other state-of-the-art methods widely used in the fault diagnosis of RCs.