Username   Password       Forgot your password?  Forgot your username? 


Engine Life Prediction based on Degradation Data

Volume 14, Number 12, December 2018, pp. 2905-2914
DOI: 10.23940/ijpe.18.12.p1.29052914

Yanhua Cao, Jinmao Guo, Yong Li, and Huiqiang Lv

Department of Equipment Support and Remanufacture, Academy of Army Armored Forces, Beijing, 100072, China

(Submitted on September 3, 2018; Revised on October 8, 2018; Accepted on November 17, 2018)


The motor hour (working time) of an armored vehicle’s engine reflects its technical state to a certain extent. However, even the same type of engine with the same motor hour shows very different technical states in different working environments. At the same time, it is difficult to obtain the full life data or physical failure mechanism required by the traditional life prediction method. In view of the above problems, a model of engine life prediction based on degradation data and neural networks is built in this paper. Firstly, the degradation parameters are selected according to certain principles, and the sample data are standardized. Then, the principal component analysis method is used to simplify multiple parameters to a comprehensive parameter, and the interpolation method is applied to get the parameter’s time series data as the train data of the neural network. Finally, the life prediction model of the engine based on the neural network is established. The validation results indicate that the model runs accurately. It is also practical and worthy of being used abroad.


References: 16

                  1. Y. B. Liu, J. M. Liu, and X. Y. Qiao, “Application of Supportive Vector Machine in Technical States Evaluation of Diesel Engine,” Journal of Academy of Armored Force Engineering, Vol. 23, No. 2, pp. 38-40, 2009
                  2. F. Zhao and H. W. Wang, “Research on Condition based Maintenance for Aero-Engine Using Hidden Markov,” Aeronautical Computing Technique, Vol. 40, No. 5, pp. 15-19, 2010
                  3. X. Liang, X. S. Li, and L. Zhang, “Survey of Fault Prognostics Supporting Condition based Maintenance,” Measurement & Control Technology, Vol. 26, No. 6, pp. 5-8, 2007
                  4. Y. H. Cao, “Research on Autonomic Logistics Key Technologies for Armored Equipment,” Ph.D. Dissertation, Academy of Armored Forces Engineering, 2012
                  5. H. W. Wang and K. N. Teng, “Review of Reliability Evaluation Technology based on Accelerated Degradation Data,” Systems Engineering and Electronics, Vol. 39, No. 12, pp. 2877-2885, 2017
                  6. Z. G. Guo, “Reliability Analysis of Barrel’s Life based on Performance Degradation Data,” Master’s Thesis, Nanjing University of Science and Technology, 2011
                  7. X. X. Kou and X. P. He, “Time Series Prediction based on RBM Neural Network,” Mathematics in Practice and Theory, Vol. 46, No. 9, pp. 173-178, 2016
                  8. W. B. Chen, “The Principle and Practice of Artificial Neural Network,” Xidian University Press, Xi’an, pp. 44-64, 2016
                  9. Y. H. Cao, S. X. Zhang, and Y. Li, “Design of Engine Condition Detection for a Certain Type of Armored Vehicle,” in Proceedings of OSEC 2017, pp. 15-18, 2017
                  10. W. B. Zhang and H. Y. Chen, “Statistical Analysis of Practical Data and Application of SPSS12.0,” The Posts and Telecommunications Press, Beijing, 2006
                  11. X. P. Zheng, J. J. Gao, and M. T. Liu, “Accident Prediction Theory and Method,” Tsinghua University Press, Beijing, 2009
                  12. D. Zhang, C. S. Zu, and C. C. Zhao, “Principal Component and Neural Network Combined Fuel Consumption Forecast,” Agricultural Equipment & Vehicle Engineering, Vol. 53, No. 6, pp. 47-52, 2015
                  13. J. J. Jiang, “Research on Prediction of Lead-Acid Batteries’ Remaining Discharge Time”, Electrical & Energy Management Technology, No. 7, pp. 73-78, 2017
                  14. Z. L. Huang, Z. B. Wang, and J. W. Wang, “Review of Reliability Evaluation Methods based on Performance Degradation Data,” Electrical & Energy Management Technology, No. 19, pp. 35-40, 2017
                  15. Y. H. Cao, Y. Li, Y. Zheng, and X. Zan, “Runtime Forecast of Military Vehicle Diesel Engine based on BP Neural Network,” in Proceedings of QR2MSE 2016, pp. 509-512, 2016
                  16. H. Y. Chen, J. M. Zhu, and Z. N. Ding, “A Survey of Researches on Combination Forecasting Models and Methodologies,” College Mathematics, Vol. 33, No. 4, pp. 1-9, 2017


                                  Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

                                  This site uses encryption for transmitting your passwords.