Elements of gas turbine degradation, such as compressor fouling, are recoverable through maintenance actions like compressor washing. These actions increase the usable engine life and optimise the performance of the gas turbine. However, these maintenance actions are performed by a separate organization to those undertaking fleet management operations, leading to significant uncertainty in the maintenance state of the asset. The uncertainty surrounding maintenance actions impacts prognostic efficacy. In this paper, we adopt Bayesian on-line change point detection to detect the compressor washing events. Then, the event detection information is used as an input to a prognostic algorithm, advising an update to the estimation of remaining useful life. To illustrate the capability of the approach, we demonstrated our on-line Bayesian change detection algorithms on synthetic and real aircraft engine service data, in order to identify the compressor washing events for a gas turbine and thus provide demonstrably improved prognosis.
How to Cite
Bayesian inference, Data-driven prognostics, change detection
Barry, D., & Hartigan, J. A. (1993). A Bayesian analysis for change point problems. Journal of the American Statistical Association, 88(421), 309–319.
Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: theory and application. Upper Saddle River, NJ, USA: Prentice-Hall, Inc.
Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17, 2002.
Brodsky, B. E., & Darkhovsky, B. S. (1993). Nonparametric Methods in Change-point Problems. Dordrecht: Kluwer.
Chen, J., & Gupta, A. K. (1997). Testing and locating variance changepoints with application to stock prices. Journal of the American Statistical Association, 92(438), 739 – 747.
Chowdhury, M. F., Selouani, S., & O’Shaughnessy, D. (2012). Bayesian on-line spectral change point detection: a soft computing approach for on-line asr. Int. J. Speech Technol., 15(1), 5–23.
Fearnhead, P., & Clifford, P. (2003). On-Line Inference for Hidden Markov Models via Particle Filters. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 65(4), 887–899.
Fearnhead, P., & Liu, Z. (2007). On-line inference for multi- ple change points problems. Journal of the Royal Statistical Society B, 69, 589–605.
Fujimaki, R. (2005). An approach to spacecraft anomaly detection problem using kernel feature space. In Proceedings pakdd-2005: Ninth pacific-asia conference on knowledge discovery and data mining. ACM Press.
GE. (2008). Flight Operations Newsletter. GE Flight Operations Support, 3(1), 1-12.
Gustafsson, F. (2000). Adaptive Filtering and Change Detection. Wiley.
Kawahara, Y., & Sugiyama, M. (2012). Sequential change- point detection based on direct density-ratio estimation. Stat. Anal. Data Min., 5(2), 114–127.
Ke, Y., Sukthankar, R., & Hebert, M. (2007). Event detection in crowded videos. In Ieee international conference on computer vision.
Kurz, R., & Brun, K. (2001). Degradation in gas turbine systems. Journal of Engineering for Gas Turbines and Power(Transactions of the ASME), 123(1), 70–77.
Malinge, Y., & Courtenay, C. (2007). Safety first the airbus safety magazine.
Marinai, L., Singh, R., Curnock, B., & Probert, D. (2003). Detection and prediction of the performance deterioration of a turbofan engine.
Reeves, J., Chen, J., Wang, X. L., Lund, R., & QiQi, L. (2007). A review and comparison of changepoint detection techniques for climate data. Journal of Applied Meteorology and Climatology, 46(6), 900–915.
Ruggieri, E. (2013). A Bayesian approach to detecting change points in climatic records. Int. J. Climatol., 33(2), 520–528.
Schwabacher, M., & Goebel, K. (2007). A survey of artificial intelligence for prognostics. In Proceedings of AAAI fall symposium.
Tobon-Mejia, D., Medjaher, K., Zerhouni, N., & Tripot, G. (2011). Hidden markov models for failure diagnostic and prognostic. In Prognostics and system health management conference (phm-shenzhen), 2011 (p. 1 -8).
Turner, R., Saatci, Y., & Rasmussen, C. E. (2009). Adaptive sequential Bayesian change point detection.
Wilson, R. C., Nassar, M. R., & Gold, J. I. (2010). Bayesian online learning of the hazard rate in change-point problems. Neural computation, 22(9), 2452–2476.
Xuan, X., & Murphy, K. (2007). Modeling changing dependency structure in multivariate time series. In Proceedings international conference in machine learning.
Zaidan, M., Mills, A. R., & Harrison, R. (2013). Bayesian Framework for Aerospace Gas Turbine Engine Prognostics. In Proceedings aerospace conference, 2013 IEEE.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.