Browse > Article
http://dx.doi.org/10.5293/IJFMS.2012.5.4.143

Machine Condition Prognostics Based on Grey Model and Survival Probability  

Tangkuman, Stenly (Department of Mechanical Engineering, San Ratulangi University)
Yang, Bo-Suk (Department of Mechanical & Automotive Engineering, Pukyong National University)
Kim, Seon-Jin (Department of Mechanical & Automotive Engineering, Pukyong National University)
Publication Information
International Journal of Fluid Machinery and Systems / v.5, no.4, 2012 , pp. 143-151 More about this Journal
Abstract
Predicting the future condition of machine and assessing the remaining useful life are the center of prognostics. This paper contributes a new prognostic method based on grey model and survival probability. The first step of the method is building a normal condition model then determining the error indicator. In the second step, the survival probability value is obtained based on the error indicator. Finally, grey model coupled with one-step-ahead forecasting technique are employed in the last step. This work has developed a modified grey model in order to improve the accuracy of prediction. For evaluating the proposed method, real trending data of low methane compressor acquired from condition monitoring routine were employed.
Keywords
Prognostics; Grey model; Survival probablity; Condition monitoring; Maintenance;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Yang, B. S., and Widodo, A., 2010, "Introduction of Intelligent Machine Fault Diagnosis and Prognosis," Nova Science Publishers, New York, USA.
2 Jardine, A. K. S., Lin, D., and Banjevic, D., 2006, "A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance," Mechanical Systems and Signal Processing, Vol. 20, pp. 1483-1510.   DOI   ScienceOn
3 Widodo, A., and Yang, B. S., 2011, "Machine Health Prognostics using Survival Probability and Support Vector Machine," Expert System with Application, Vol. 38, pp. 8430-8437.   DOI   ScienceOn
4 Peng, Y., Dong, M., and Zuo, M. J., 2010, "Current Status of Machine Prognostics in Condition-based Maintenance: a Review," International Journal of Advanced Manufacturing Technology, Vol. 50, pp. 297-313.   DOI
5 Byington, C. S., Watson, M., and Edwards, D., 2004, "Data-Driven Neural Network Methodology to Remaining Life Predictions for Aircraft Actuator Components," Proceedings of the IEEE Aerospace Conference, Vol. 6, pp. 3581-3589.
6 Gebraeel, N. Z., Lawley, M. A., Li, R., and Ryan, J. K., 2005, "Residual-Life Distributions from Component Degradation Signals: a Bayesian approach," IIE Transactions, Vol. 37, pp. 543-557.   DOI   ScienceOn
7 Huang, R. Q., and Xi, L. F., 2007, "Residual Life Predictions for Ball Bearing Based On Neural Networks," Chinese Journal of Mechanical Engineering, Vol. 43, No. 10, pp. 137-143.   DOI   ScienceOn
8 Gebraeel, N. Z., and Lawley, M. A., 2008, "A Neural Network Degradation Model for Computing and Updating Residual Life Distributions," IEEE Transactions on Automation Science and Engineering, Vol. 5, No. 1, pp. 387-401.
9 Baruah, P., and Chinnam, R. B., 2005, "HMMs for Diagnostics and Prognostics in Machining Processes," International Journal of Production Research, Vol. 43, No. 6, pp. 1275-1293.   DOI   ScienceOn
10 Liao, H., Zhao, W., and Guo, H., 2006, "Predicting Remaining Useful Life of an Individual Unit Using Proportional Hazards Model and Logistic Regression Model," IEEE 1-4244-0008-2/06, pp. 127-132.
11 Ku, L. L., and Huang, T. C., 2006, "Sequential Monitoring of Manufacturing Processes: an Application of Grey Forecasting Models," International Journal of Advanced Manufacturing Technology, Vol. 27, No. 5-6, pp. 543-546.   DOI
12 Gu, J., Vichare, N., Ayyub, B., and Pecht, M., 2010, "Application of Grey Prediction Model for Failure Prognostics of Electronics," International Journal of Performability Engineering, Vol. 6, No. 5, pp. 435-442.
13 Deng, J. L., 1989, "Introduction to Grey System Theory," Journal of Grey System, Vol. 1, No. 1, pp. 1-24.
14 Mao M., and Chirwa, E. C., 2006, "Application of Grey Model GM(1,1) to Vehicle Fatality Risk Estimation," Technological Forecasting and Social Change, Vol. 73, pp. 588-605.   DOI   ScienceOn
15 Si, X. S., Wang, W., Hu, C. H., and Zhou, D. H., 2011, "Remaining Useful Life Estimation-A Review on the Statistical Data Driven Approaches," European Journal of Operational Research, Vol. 213, pp. 1-14.   DOI   ScienceOn
16 Lee, E. T., and Wang, J. W., 2003, "Statistical Methods for Survival Data Analysis, 3rd Ed.," John Wiley & Sons, Inc, New Jersey, USA.
17 Tran, V. T., Yang, B. S., Oh, M. S., and Tan, A. C. C., 2008, "Machine Condition Prognosis Based on Regression Trees and One-step-ahead Prediction," Mechanical Systems and Signal Processing, Vol. 22, pp. 1179-1193.   DOI   ScienceOn