Fault Diagnosis of Marine Diesel Engine Based on Mixed Similarity Algorithm
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.
 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
 Caocan. The Application of Grey Correlation Model in Fault Diagnosis Decision[A]. Chinese Association of Automation,2015:5
 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.
 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
Copyright (c) 2019 EPH - International Journal of Science And Engineering (ISSN: 2454 - 2016)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
- All contributor(s) agree to transfer the copyright of this article to EPH Journal.
- EPH Journal will have all the rights to distribute, share, sell, modify this research article with proper reference of the contributors.
- EPH Journal will have the right to edit or completely remove the published article on any misconduct happening.