Fault diagnosis of diesel engine lubrication system based on PSO-SVM and centroid location algorithm

Yingmin Wang, Tao Cui, Fujun Zhang, Tianpu Dong, Shen Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

Fault of diesel engine lubrication system will affect engine performance, and diesel engine operation parameters reflect the working state of the engine. In this paper, a data-driven fault diagnosis is proposed using engine real working data. Considering the randomness and instability of the oil pressure in the lubrication system, a fault diagnosis method based on PSO-SVM model and centroid location algorithm is presented. Firstly, fault features are extracted analyzing the data in normal condition. Secondly, particle swarm optimization (PSO) algorithm is used to search the best parameters of support vector machine (SVM) to establish the model of fault diagnosis. Then, support vector machine classification interface is fitted to a curve, and the boundary conditions of fault diagnosis are obtained. Finally, the typical faults of diesel engine lubrication system are diagnosed by the proposed fault diagnosis algorithm. The results show that he proposed PSO-SVM model achieved above 95% classification accuracy; and two typical lubrication system faults of diesel engine can be diagnosed based on the proposed diagnosis method.

Original languageEnglish
Title of host publication2016 International Conference on Control, Automation and Information Sciences, ICCAIS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages221-226
Number of pages6
ISBN (Electronic)9781509006502
DOIs
Publication statusPublished - 17 Jan 2017
Event5th International Conference on Control, Automation and Information Sciences, ICCAIS 2016 - Ansan, Korea, Republic of
Duration: 27 Oct 201629 Oct 2016

Publication series

Name2016 International Conference on Control, Automation and Information Sciences, ICCAIS 2016

Conference

Conference5th International Conference on Control, Automation and Information Sciences, ICCAIS 2016
Country/TerritoryKorea, Republic of
CityAnsan
Period27/10/1629/10/16

Keywords

  • centroid location
  • diesel engine
  • fault diagnosis
  • lubrication system
  • particle swarm optimization
  • support vector machine

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Wang, Y., Cui, T., Zhang, F., Dong, T., & Li, S. (2017). Fault diagnosis of diesel engine lubrication system based on PSO-SVM and centroid location algorithm. In 2016 International Conference on Control, Automation and Information Sciences, ICCAIS 2016 (pp. 221-226). Article 7822464 (2016 International Conference on Control, Automation and Information Sciences, ICCAIS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCAIS.2016.7822464