Deep Fusion of Intrinsic Vibration Information and Grassmann Manifold-based Similarity for Fault Identification of Reciprocating Compressor

Abstract

This paper introduces a new method based on deep belief networks (DBNs) to integrate intrinsic vibration information and assess the similarity of subspaces established on the Grassmann manifold for intelligent fault diagnosis of a reciprocating compressor (RC). Initially, raw vibration signals undergo empirical mode decomposition to break them down into multiple intrinsic mode functions (IMFs). This operation can reveal inherent vibration patterns of fault and other components hidden in the original signals. Subsequently, features are refined from all the IMFs and concatenated into a high-dimensional representative vector, offering localized and comprehensive insights into RC operation. Through DBN, the fault-sensitive information is further refined from the features to enhance their performance in fault identification. Finally, similarities among subspaces on the Grassmann manifold are computed to match fault types. The efficacy of the method is validated using field data. Comparative analysis with traditional approaches for feature dimension reduction, feature extraction, and Euclidean distance-based fault identification underscores the effectiveness and superiority of the proposed method in RC fault diagnosis.