Monitoring gas turbine combustors' health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustors’ abnormalities is through continuously monitoring exhaust gas temperature profiles. Over the years many anomaly detection technologies have been explored for detecting combustor faults, however, the performance (detection rate) of anomaly detection solutions fielded is still inadequate. Advanced technologies that can improve detection performance are in great need. Aiming for improving anomaly detection performance, in this paper we introduce recently-developed deep learning (DL) in machine learning into the combustors’ anomaly detection application. Specifically, we use deep learning to hierarchically learn features from the sensor measurements of exhaust gas temperatures. And we then use the learned features as the input to a neural network classifier for performing combustor anomaly detection. Since such deep learned features potentially better capture complex relations among all sensor measurements and the underlying combustors’ behavior than handcrafted features do, we expect the learned features can lead to a more accurate and robust anomaly detection. Using the data collected from a real-world gas turbine combustion system, we demonstrated that the proposed deep learning based anomaly detection significantly indeed improved combustors’ anomaly detection performance.Deep learning, one of the breakthrough technologies in machine learning, has attracted tremendous research interests in recent years in the domains such as computer vision, speech recognition and natural language processing.Deep learning, to the best of our knowledge, has not been used for any PHM applications, however. It is our hope that our initial work presented in this paper would shed some light on how deep learning as an advanced machine learning technology can benefit PHM applications and, more importantly, can stimulate more research interests in our PHM community.
How to Cite
anomaly detection, gas turbine combustors, deep learning, feature learning, feature engineering
Akoglu, L. Tong, H.H and Koutra, D. (2014). Graph-based anomaly detection and description: A survey, Data Mining and Knowledge Discovery, 28(4), 2014. arXiv:1404.4679
Allegorico, C. and Mantini, V. (2014). A data-driven approach for on-line gas turbine combustion monitoring using classification models. 2nd European Conference of the Prognostics and Health Management Society 2014, Nantes, France, July 8 – 10, 2014.
Arel, I., Rose, D.C. and Kamowski, T.P. (2010). Deep machine learning – a new frontier in artificial intelligence research. IEEE Computer Intelligence Magazine, Vol.5, No. 4, pp13-18.
Arranz, A., Cruz, A., Sanz-Bobi, M.A., Riuz, P. and Coutino, J. (2007). DADICO: Intelligent system for anomaly detection in a combined cycle gas turbine plant, Expert system with applications, 34(4), pp. 2267- 2277.
Brownlee, J. (2014). Discover feature engineering, how to engineer features and how to get good at it. machinelearningmastery.com/discover-feature- engineering-how-to-engineer-features-and-how-to-get- good-at-it/
Cafarella, M.J., Kumar, A., Niu, F., Park, Y., R ́e, C. and Zhang C. (2013). Brainwash: A data system for feature engineering. In CIDR 2013.
Chakraborty, S., Gupta, S., Ray, A. and Mukhopadhyay, A. (2008). Data-driven fault detection and estimationin thermal pulse combustors. Proceedings of the Institution of
Mechanical Engineers, Part G: Journal of Aerospace Engineering August 1, 2008 222: 1097- 1108, DOI: 10.1243/09544100JAERO432
Chandola, V ., Banerjee, A. and Kumar, V . (2009). Anomaly Detection : A Survey, ACM Computing Surveys, Vol. 41(3), Article 15, July 2009.
Chen, W., (1991). Nonlinear Analysis of Electronic Prognostics. Doctoral dissertation. The Technical University of Napoli, Napoli, Italy.
Coates, A., Lee, H., and Ng, A. Y. (2011). An analysis of single-layer networks in unsupervised feature learning. In AIS-TATS 14, 2011.
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K.,and Kuksa, P . (2011). Natural language processing (almost) fromscratch. Journal of Machine Learning Research, 12, 2493–2537.
Davis, S.B. and Mermelstein, P. (1980). Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken sentences. IEEE Transactions on
Acoustics, Speech, and Signal Processing, vol. ASSP-28, No.4, August 1980.
Deng, L., Seltzer, M., Yu, D., Acero, A., Mohamed, A., and Hinton, G. (2010). Binary coding of speech spectrograms using a deep auto-encoder. In Interspeech 2010, Makuhari, Chiba,
Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10): 78.
Ferrell, B. L. (1999), JSF Prognostics and Health Management. Proceedings of IEEE Aerospace Conference. March 6-13, Big Sky, MO. doi: 10.1109/AERO.1999.793190.
He, H. B. and Garcia E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 9, pp.1263-1284.
Heimerl, F., Jochim, C., Koch, S., & Ertl, T. (2012). FeatureForge: A Novel Tool for Visually Supported Feature Engineering and Corpus Revision. In COLING (Posters), pp. 461-470.
Hinton, G. E., Osindero, S., and Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527–1554.
Huang, G.B., Zhou, H.M., Ding, X.J., and Zhang R. (2012). Extreme learning machine for regression and multiclass classification, IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, Vol. 42, No. 2, April 2012, pp. 513 – 529.
Huang, G.B., Zhu, Q.Y., and Siew, C.K. (2006). Extreme learning machine: Theory and applications, Stacked denoising autoencoder for representation learning in pose-based recognition. Proceedings of IEEE 3rd Conference on Consumer Electronics (GCCE), Oct. 7- 10, 2014, Tokyo, Japan, pp. 684-688.
Neurocomputing, vol. 70, no. 1–3, pp. 489–501, Dec.2006.
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, vol. 22, pp. 679-688. doi:10.1016/j.ijforecast.2006.03.001.
ICLR (2015), International conference on learning representations. http://www.iclr.cc/doku.php.
ICML (2013), Challenges in representation learning: ICML 2013.
Lee, H., Battle, A., Raina, R., and Ng, A.Y. (2007). Efficient sparse coding algorithms. In NIPS, 2007.
Liu, X., Lin, S., Fang, J. & Xu, Z. (2015). Is extreme learning machine feasible? A theoretical assessment (Part I), IEEE Transactions on Neural Networks and Learning Systems, Vol. 25, No. 1, January 2015, pp. 7 – 19.
Lin, S., Liu, X., Fang, J. & Xu, Z. (2015). Is extreme learning machine feasible? A theoretical assessment (Part II), IEEE Transactions on Neural Networks and Learning Systems, Vol. 25, No. 1, January 2015, pp. 21 – 34.
Lowe, D. G. (1999). Object recognition from local scale- invariant features. Proceedings of the International Conference on Computer Vision 2. pp. 1150–1157.
NIPS (2014), Deep learning and representation learning workshop: NIPS 2014 (http://www.dlworkshop.org/).
Ogbonnaya, E.A., Ugwu, H.U., and Theophilus Johnson, K. (2012). Gas Turbine Engine Anomaly Detection Through Computer Simulation Technique of Statistical Correlation, IOSR Journal of Engineering, 2(4), 544- 554.
Raina, R., Battle, A., Lee, H., Packer, B., and Ng, A.Y. (2007). Self-taught learning: Transfer learning from unlabeled data. In ICML, 2007.
Tolani, D.K., Yasar, M., Ray, A. and Yang, V. (2006). Anomaly Detection in Aircraft Gas Turbine Engines, Journal of Aerospace Computing, Information, and Communication, Vol. 3, No. 2 (2006), pp. 44-51. DOI: 10.2514/1.15768.
Wang, H., Shi, X., and Yeung, D.-Y. (2015). Relational Stacked Denoising Autoencoder for Tag Recommendation. Proceedings of AAAI ’15.
Xue, F. and Yan, W. (2007). Parametric model-based anomaly detection for locomotive subsystems Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN), Orlando, FL, August 12- 17, 2007.
Yan, W., Qiu, H. and Iyer, N. (2008). Feature extraction for bearing prognostics and health management (PHM) – a survey. MFPT 2008, Virginia Beach, Virginia.
Zimek, A., Schubert, E., Kriegel, H.-P. (2012). A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining, 5 (5): 363–387. doi:10.1002/sam.11161.
Zaher A, McArthur SDJ, Infield DG, Patel Y. (2009). Online wind turbine fault detection through automated SCADA data analysis. Wind Energy, 12(6): 574–93
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.