TARGET-AWARE ANOMALY DETECTION AND DIAGNOSIS

Alexander Borisov, George Runger, Eugene Tuv

Abstract

Anomaly detection in data streams requires a signal of an unusual event, and an actionable response requires diagnostics. Furthermore, monitoring for process control is often concerned with one or more target (controlled) attributes. Consequently, it is necessary to separate anomalies (and their contributing attributes) that could influence the controlled target strongly, and this becomes more important with the increased number of monitored attributes in modern processes. This task leads to a difficult problem not addressed directly by the machine learning/process control community. We introduce the target-aware anomaly detection problem and present a solution for process control in modern systems (with nonlinear dependencies, high dimensional noisy data, missing data, and so on). The main objective is to identify and rank outliers and also diagnose their contributing attributes with respect to the possible effect on the response. The method is different from traditional linear and/or univariate approaches, as it can deal with local data structure in the neighborhood of an outlier, and can handle complex interactions via the use of an appropriate learner. In addition, the method can be computed quickly and does not require time consuming matrix operations. Comparisons are made to traditional contribution plots computed from partial least squares.

References

  1. Angelov, P., Giglio, V., Guardiola, C., Lughofer, E., and Luján, J. (2006). An approach to model-based fault detection in industrial measurement systems with application to engine test benches. Measurement Science and Technology, 17:1809.
  2. Borisov, A., Eruhimov, V., and Tuv, E. (2006). Treebased ensembles with dynamic soft feature selection. In Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L., editors, Feature Extraction Foundations and Applications: Studies in Fuzziness and Soft Computing. Springer.
  3. Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and Regression Trees. Wadsworth, Belmont, MA.
  4. Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3):15.
  5. Chiang, L., Russell, E., and Braatz, R. (2001). Fault detection and diagnosis in industrial systems. Springer Verlag.
  6. Efendic, H., Schrempf, A., and Del Re, L. (2003). Data based fault isolation in complex measurement systems using models on demand. In Proceedings of the 5th IFAC-Safeprocess 2003, IFAC, pages 1149-1154. ACM.
  7. Ergon, R. (2004). Informative pls score-loading plots for process understanding and monitoring. Journal of Process Control, 14(6):889-897.
  8. Filev, D. and Tseng, F. (2006). Novelty detection based machine health prognostics. In Evolving Fuzzy Systems, 2006 International Symposium on, pages 193- 199. IEEE.
  9. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5):1189-1232.
  10. Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning. Springer.
  11. Hodge, V. J. and Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22:85-126.
  12. Hotelling, H. (1947). Multivariate quality controlillustrated by the air testing of sample bombsights. In Eisenhart, C., Hastay, M., and Wallis, W., editors, Techniques of Statistical Analysis, pages 111- 184. McGraw-Hill, New York.
  13. Lughofer, E. and Guardioler, C. (2008). On-line fault detection with data-driven evolving fuzzy models. Control and Intelligent Systems, 36(4):307-317.
  14. Miller, P., Swanson, R., and Heckler, C. (1998). Contribution plots: A missing link in multivariate quality control. Applied Mathematics and Computer Science, 8(4):775-792.
  15. Runger, G., Alt, F., and Montgomery, D. (1996). Contributors to a Multivariate Statistical Process Control Chart Signal. Communications in Statistics-Theory and Methods, 25(10):2203-2213.
  16. Wold, S., Sjostrom, M., and Eriksson, L. (2001). PLSregression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, 58(2):109- 130.
Download


Paper Citation


in Harvard Style

Borisov A., Runger G. and Tuv E. (2011). TARGET-AWARE ANOMALY DETECTION AND DIAGNOSIS . In Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8425-74-4, pages 14-23. DOI: 10.5220/0003530100140023


in Bibtex Style

@conference{icinco11,
author={Alexander Borisov and George Runger and Eugene Tuv},
title={TARGET-AWARE ANOMALY DETECTION AND DIAGNOSIS},
booktitle={Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2011},
pages={14-23},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003530100140023},
isbn={978-989-8425-74-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - TARGET-AWARE ANOMALY DETECTION AND DIAGNOSIS
SN - 978-989-8425-74-4
AU - Borisov A.
AU - Runger G.
AU - Tuv E.
PY - 2011
SP - 14
EP - 23
DO - 10.5220/0003530100140023