Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network

Van Tung Tran (Corresponding Author), Faisal Al Thobiani, Tiedo Tinga, Andrew David Ball, Gang Niu

    Research output: Contribution to journalArticleAcademicpeer-review

    13 Citations (Scopus)
    3 Downloads (Pure)

    Abstract

    In this paper, a hybrid deep belief network is proposed to diagnose single and combined faults of suction and discharge valves in a reciprocating compressor. This hybrid integrates the deep belief network structured by multiple stacked restricted Boltzmann machines for pre-training and simplified fuzzy ARTMAP (SFAM) for fault classification. In the pre-training procedure, an algorithm for selecting local receptive fields is used to group the most similar features into the receptive fields of which top values are the units of each layer, and then restricted Boltzmann machine is applied to these units to construct a network. Unsupervised learning is also carried out for each restricted Boltzmann machine layer in this procedure to compute the network weights and biases. Finally, the network output is fed into SFAM to perform fault classification. In order to diagnose the valve faults, three signal types of vibration, pressure, and current are acquired from a two-stage reciprocating air compressor under different valve conditions such as suction leakages, discharge leakages, spring deterioration, and their combination. These signals are subsequently processed so that the useful fault information from the signals can be revealed; next, statistical features in the time and frequency domains are extracted from the signals and used as the inputs for hybrid deep belief network. Performance of hybrid deep belief network in fault classification is compared with that of the original deep belief network and the deep belief network combined with generalized discriminant analysis, where softmax regression is used as a classifier for the latter two models. The results indicate that hybrid deep belief network is more capable of improving the diagnosis accuracy and is feasible in industrial applications.
    Original languageEnglish
    Pages (from-to)3767-3780
    JournalProceedings of the Institution of Mechanical Engineers. Part C: Journal of mechanical engineering science
    Volume232
    Issue number20
    Early online date20 Nov 2017
    DOIs
    Publication statusPublished - 1 Oct 2018

    Keywords

    • Fault diagnosis
    • Reciprocating compressor
    • Hybrid deep belief networks
    • Local receptive fields
    • Simplified fuzzy ARTMAP

    Fingerprint Dive into the research topics of 'Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network'. Together they form a unique fingerprint.

    Cite this