A Condition-Monitoring Approach for Diesel Engines Based on an Adaptive VMD and Sparse Representation Theory

Xiao Yang, Fengrong Bi, Yabing Jing, Xin Li and Guichang Zhang
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Xiao Yang: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Fengrong Bi: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Yabing Jing: Tianjin Internal Combustion Engine Research Institute, Tianjin 300072, China
Xin Li: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Guichang Zhang: College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China

Energies, 2022, vol. 15, issue 9, 1-20

Abstract: This paper presents a novel method for condition monitoring using the RMS residual of vibration signal reconstruction based on trained dictionaries through sparse representation theory. Measured signals were firstly decomposed into intrinsic mode functions (IMFs) for training the initial dictionary. In this step, an adaptive variational mode decomposition (VMD) was proposed for providing information with higher accuracy, and the decompositions were used as discriminative atoms for sparse representation. Then, the overcomplete dictionary for sparse coding was learned from IMFs to reserve the highlight feature of the signals. As the dictionaries were trained, newly measured signals could be directly reconstructed without any signal decompositions or dictionary learning. This meant errors likely introduced by signal process techniques, such as VMD, EMD, etc., could be excluded from the condition monitoring. Moreover, the efficiency of the fault diagnosis was greatly improved, as the reconstruction was fast, which showed a great potential in online diagnosis. The RMS of the residuals between the reconstructed and measured signals was extracted as a feature of condition. A case study on operating condition identification of a diesel engine was carried out experimentally based on vibration accelerations, which validated the availability of the proposed feature extraction and condition-monitoring approach. The presented results showed that the proposed method resulted in a great improvement in the fault feature extraction and condition monitoring, and is a promising approach for future research.

Keywords: condition monitoring; vibration; signal reconstruction; variational mode decomposition; sparse representation; diesel engine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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