An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring
Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.
KeywordsRemaining useful life Prediction Artificial neural network Accurate Bearing
Unable to display preview. Download preview PDF.
- Banjevic D., Jardine A. K. S., Makis V. (2001) A control-limit policy and software for condition-based maintenance optimization. INFOR 39: 32–50Google Scholar
- Kacprzynski, G. J., Roemer, M. J., Modgil, G., Palladino, A., & Maynard, K. (2002). Enhancement of physics-of-failure prognostic models with system level features. In Proceedings of the 2002 IEEE aerospace conference, Big Sky, MT, USA.Google Scholar
- Kuo W., Zuo M. J. (2003) Optimal reliability modeling: Principles and applications. Wiley, HobokenGoogle Scholar
- Levitin G. (2005) Universal generating function in reliability analysis and optimization. Springer-Verlag, LondonGoogle Scholar
- Makis V., Jardine A. K. S. (1992) Optimal replacement in the proportional hazards model. INFOR 30: 172–183Google Scholar
- Marble, S., & Morton, B. P. (2006). Predicting the remaining life of propulsion system bearings. In Proceedings of the 2006 IEEE aerospace conference, Big Sky, MT, USA.Google Scholar
- Rojas R. (1996) Neural networks: A system introduction. Springer, BerlinGoogle Scholar
- Stevens, B. (2006). EXAKT reduces failures at Canadian Kraft Mill. http://www.omdec.com.
- Tian, Z., & Zuo, M. J. (2009). Health condition prognostics of gears using a recurrent neural network approach. In Proceedings of the reliability and maintainability symposium, Fort Worth, TX, USA.Google Scholar