Identification of start of combustion based on contribution level of principal component in vibration signal of cylinder head surface

Pan, Bangxiong; Xiaodan, Zhao; Wang, Limei; Zhao, Xinbing

doi:10.1016/j.apacoust.2022.108632

Cited by 3 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For power machinery such as diesel engines, effective condition monitoring can be confirmed by both intrusive and indirect measurement methods [1,2], including incylinder pressure [3], acoustic emission [4], engine speed fluctuation [5], vibration signal sensors [6], and so on. Among these approaches, methods using vibration signals have attracted extensive attention due to their simplicity, high signal-to-noise ratio, and abundant information provided.…”

Section: Introductionmentioning

confidence: 99%

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

Xinfeng

Yabing

et al. 2022

Energies

View full text Add to dashboard Cite

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.

show abstract