Abstract
This paper proposes a three-stage method involved system identification techniques, proportional hazard model, and support vector machine for assessing the machine health degradation and forecasting the machine remaining useful life (RUL). In the first stage, only the normal operating condition of machine is used to create identification model to mimic the dynamic system behaviour. The machine degradation is indicated by degradation index which is the root mean square of residual errors. These errors are the difference between identification model and behaviour of system. In the second stage, the Cox’s proportional hazard model is generated to estimate the survival function of the system. Finally, support vector machine, one of the remarkable machine learning techniques, in association with direct prediction method of time-series techniques is utilized to forecast the RUL. The data of low methane compressor acquired from condition monitoring routine are used for appraising the proposed method. The results indicate that the proposed method could be used as a potential tool to machine prognostics.
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Acknowledgments
The authors gratefully acknowledge the financial support from Brain Korea (BK) 21 and The Vietnam National Foundation for Science and Technology Development (NAFOSTED) for this study.
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Tran, V.T., Pham, H.T., Yang, B.S., Nguyen, T.T. (2012). Machine Performance Degradation Assessment and Remaining Useful Life Prediction Using Proportional Hazard Model and SVM. In: Mathew, J., Ma, L., Tan, A., Weijnen, M., Lee, J. (eds) Engineering Asset Management and Infrastructure Sustainability. Springer, London. https://doi.org/10.1007/978-0-85729-493-7_74
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DOI: https://doi.org/10.1007/978-0-85729-493-7_74
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