Fault Diagnosis of Marine Diesel Engine Based on Mixed Similarity Algorithm

  • Cuijia * Jiangsu University of Science and Technology Zhenjiang
  • Chen chao Jiangsu University of Science and Technology Zhenjiang
  • Ji peng Jiangsu University of Science and Technology Zhenjiang
Keywords: Diesel engine, Pearson correlation coefficient, Grey correlation diagnosis, Collaborative filtering, Mixed similarity

Abstract

In view of the problems of inlet and exhaust faults and clogging of the marine diesel engine, the appropriate thermal parameters are selected as the basis for fault diagnosis and positioning. In this paper, the improved Pearson correlation coefficient and grey relational diagnosis analysis are combined, and a hybrid similarity collaborative filtering algorithm is proposed. At the same time, the simulation model of the diesel engine is built by AVL-BOOST software, and the fault samples are simulated. The mixed similarity collaborative filtering algorithm is used to calculate the correlation degree of the fault data, and the final diagnosis result is given accordingly. The results show that the hybrid similarity diagnosis algorithm has excellent diagnosis speed and accuracy, which can ensure the fault diagnosis and location of diesel engine is more accurate and reliable.

Downloads

Download data is not yet available.

Author Biographies

Cuijia *, Jiangsu University of Science and Technology Zhenjiang

School of Electronics and Information, Jiangsu University of Science and Technology Zhenjiang 212000,China

Chen chao, Jiangsu University of Science and Technology Zhenjiang

School of Electronics and Information, Jiangsu University of Science and Technology Zhenjiang 212000,China

Ji peng, Jiangsu University of Science and Technology Zhenjiang

School of Electronics and Information, Jiangsu University of Science and Technology Zhenjiang 212000,China

References

Maritime University | J Shanghai Mari Univ, 2017, 38(03):85-89.
[6] Hanmin ,Li Jinbing, Xu Meiling. Fault Prognosis of Marine Diesel Engine with Working State Transition Based on EIIKF[J]. ACTA AUTOMATICA SINICA, 2019,1-7
[7] Caocan. The Application of Grey Correlation Model in Fault Diagnosis Decision[A]. Chinese Association of Automation,2015:5
[8] Fu Yunxiao,Jia Limin. Roller Bearing Fault Diagnosis Method Based on LMD-CM-PCA[J]. Journal of Vibration, Measurement & Diagnosis, 017, 37(02): 249-255+400-401.
[9] Yuandui,Wang Yeqiu,Tang Xinfei.Research on simulation method for thermal failure of marine medium speed diesel engine[J]. China Ship Repair, 2018, 31(04):41-4
Published
2019-02-25
How to Cite
[1]
C. *, C. chao, and J. peng, “Fault Diagnosis of Marine Diesel Engine Based on Mixed Similarity Algorithm”, se, vol. 5, no. 2, pp. 36-46, Feb. 2019.