On Optimizing Anomaly Detection Rules for Gas Turbine Health Monitoring
Gas turbine health monitoring is a critical process in preventing costly unplanned maintenance and secondary damage. To monitor gas turbine health, control signals are typically collected and analyzed using anomaly detection rules and models to assess failure likelihood based on observed data patterns. An analytic designer will often deal with rule optimization tasks in order to maximize failure detection and reduce false alarms. Manual tradeoff analysis is typically time consuming and suboptimal. In this paper, we attempt to address this issue by introducing a strategy for automatic and efficient rule optimization. By focusing on optimizing rule parameters while keeping rule structure intact, we maximize the rule performance by integrating domain knowledge with data driven optimization techniques. Realizing that automated rule tuning can be computationally expensive and infeasible to complete in reasonable time, we will leverage our recently-developed scalable learning framework - iScale that allows for automatically distributing rule tuning tasks to a large number of cloud computers, which not only dramatically speeds up tuning process, but also enables us to handle big size of historical data for tuning. We also explore different search methods to make rule tuning more efficient and effective and finally demonstrate our rule optimization strategy by a real-world application.
How to Cite
anomaly detection, gas turbine, health monitoring, optimization
Das, S. and Suganthan, P.N. (2011), "Differential Evolution: A Survey of the State-of-the-Art," IEEE Trans. On Evolutionary Computation, vol.15, no.1, pp.4-31, Feb. 2011.
Marler, R.T. and Arora, J.S. (2004), Survey of multi- objective optimization methods for engineering, Structural and Multidisciplinary Optimization, 26 (6) (2004), pp. 369–395.
Rao, S. (2009), Engineering Optimization: Theory and Practice. 4th Edition, John Wiley & Sons, Inc., Hoboken, New Jersey.
Reyes-Sierra, M. and Coello, C.A.C. (2006), Multi- objective Particle Swarm Optimizers: A Survey of the State-of-the-Art, International Journal of Computational Intelligence Research, Vol.2, No.3, 2006, pp.287-308.
Storn, R. and Price, K. (1997), “Differential Evolution, A Simple and efficient Heuristic Strategy for Global Optimization over Continuous Spaces", Journal of Global Optimization, Vol. 11, pp. 341-359.
Xue, F., Sanderson, A. C., and Graves, R. J. (2003), “Pareto-based multiobjective differential evolution.” Proc., 2003 Congress on Evolutionary Computation (CEC’2003), Vol. 2, IEEE, New York, 862–869.
Yan, W., Iyer, N., Bonissone, P. and Varma, A. (2011), "iScale – Next Generation Framework for Creating Machine Learning Models", GE Global Research Center Whitepaper 2011.
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.