Abstract
Anomaly detection is the problem of finding patterns in data that do not conform to expected behavior. Similarly, when patterns are numerically distant from the rest of sample, anomalies are indicated as outliers. Anomaly detection had recently attracted the attention of the research community for real-world applications. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct, or react to the situations associated with them. In that sense, heavy extraction machines for pumping and generation operations like turbomachines are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. For dealing with this and with the lack of labeled data, in this paper we propose a combination of a fast and high quality segmentation algorithm with a one-class support vector machine approach for efficient anomaly detection in turbomachines. As result we perform empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S.: A geometric framework for unsupervised anomaly detection. In: Applications of Data Mining in Computer Security, pp. 77–101. Springer (2002)
King, S., King, D., Astley, K., Tarassenko, L., Hayton, P., Utete, S.: The use of novelty detection techniques for monitoring high-integrity plant. In: Proceedings of the 2002 International Conference on Control Applications, vol. 1, pp. 221–226. IEEE (2002)
Borrajo, M.L., Baruque, B., Corchado, E., Bajo, J., Corchado, J.M.: Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises. International Journal of Neural Systems 21(4), 277–296 (2011)
Woźniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Information Fusion 16, 3–17 (2014)
Calvo-Rolle, J.L., Corchado, E.: A bio-inspired knowledge system for improving combined cycle plant control tuning. Neurocomputing 126, 95–105 (2014)
Keogh, E., Lonardi, S., Chiu, B.: c.: Finding surprising patterns in a time series database in linear time and space. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 550–556. ACM (2002)
Ratsch, G., Mika, S., Scholkopf, B., Muller, K.: Constructing boosting algorithms from svms: an application to one-class classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(9), 1184–1199 (2002)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41(3), 15 (2009)
Grubbs, F.E.: Procedures for detecting outlying observations in samples. Technometrics 11(1), 1–21 (1969)
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: Identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD 2000, pp. 93–104. ACM, New York (2000)
Papadimitriou, S., Kitagawa, H., Gibbons, P., Faloutsos, C.: LOCI: Fast outlier detection using the local correlation integral. In: Proceedings 19th International Conference on Data Engineering (ICDE 2003), pp. 315–326. IEEE Press (2003)
Ringberg, H., Soule, A., Rexford, J., Diot, C.: Sensitivity of pca for traffic anomaly detection. In: ACM SIGMETRICS Performance Evaluation Review, vol. 35, pp. 109–120. ACM (2007)
Fujimaki, R., Yairi, T., Machida, K.: An approach to spacecraft anomaly detection problem using kernel feature space. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 401–410. ACM (2005)
Barbara, D., Wu, N., Jajodia, S.: Detecting novel network intrusions using Bayes estimators. In: First SIAM Conference on Data Mining. SIAM (2001)
Roth, V.: Outlier detection with one-class kernel Fisher discriminants. In: Advances in Neural Information Processing Systems, vol. 17, pp. 1169–1176. MIT Press (2005)
Bouchard, D.: Automated time series segmentation for human motion analysis. Center for Human Modeling and Simulation, University of Pennsylvania (2006)
Bingham, E., Gionis, A., Haiminen, N., Hiisilä, H., Mannila, H., Terzi, E.: Segmentation and dimensionality reduction. In: SDM. SIAM (2006)
Lemire, D.: A better alternative to piecewise linear time series segmentation. In: SDM. SIAM (2007)
Hunter, J., McIntosh, N.: Knowledge-based event detection in complex time series data. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J.C. (eds.) AIMDM 1999. LNCS (LNAI), vol. 1620, pp. 271–280. Springer, Heidelberg (1999)
Vlachos, M., Lin, J., Keogh, E., Gunopulos, D.: A wavelet-based anytime algorithm for k-means clustering of time series. In: Proc. Workshop on Clustering High Dimensionality Data and Its Applications. Citeseer (2003)
Bollobás, B., Das, G., Gunopulos, D., Mannila, H.: Time-series similarity problems and well-separated geometric sets. In: Proceedings of the Thirteenth Annual Symposium on Computational Geometry, pp. 454–456. ACM (1997)
Martí, L.: Scalable Multi-Objective Optimization. PhD thesis, Departmento de Informtica, Universidad Carlos III de Madrid, Colmenarejo, Spain (2011)
Neyman, J.: Outline of a theory of statistical estimation based on the classical theory of probability. Philosophical Transactions of the Royal Society A 236, 333–380 (1937)
Chambers, J., Cleveland, W., Kleiner, B., Tukey, P.: Graphical Methods for Data Analysis, Wadsworth, Belmont (1983)
Di Eugenio, B., Glass, M.: The Kappa statistic: A second look. Computational Linguistics 30(1), 95–101 (2004)
Salzberg, S.L.: On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery 1(3), 317–328 (1997)
McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153–157 (1947)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Martí, L., Sanchez-Pi, N., Molina, J.M., García, A.C.B. (2014). Combining Support Vector Machines and Segmentation Algorithms for Efficient Anomaly Detection: A Petroleum Industry Application. In: de la Puerta, J., et al. International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-07995-0_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07994-3
Online ISBN: 978-3-319-07995-0
eBook Packages: EngineeringEngineering (R0)