Intelligent fault detection of reciprocating compressor using a novel discrete state space

Abstract

Aiming to the intelligent fault detection of reciprocating compressors (RCs), a novel method based on discrete state space construction and state space transformation is proposed in this paper. Conventional fault detection methods were conducted with continuous data, which tend to cause overfitting on establishing a model. This paper proposes a novel framework of fault detection on the basis of discrete data. High-dimensional features are extracted from raw signals and discretized into labels representing potential states of operating conditions. These discrete features of normal data are constructed into raw state space, and then transformed into a new state space by latent Dirichlet allocation subsequently. The transformed state space and its distribution can reflect operating conditions with more representative information. Then the distributions of real-time data under the transformed normal state space are calculated. The divergences of real-time state space distribution and normal state space are determined to identify the operating condition of reciprocating compressors, including normal condition and abnormal condition. This proposed method was validated on four cases of RC faults. The result demonstrates the effectiveness of the proposed method on detecting abnormal conditions of RCs.