Mining abnormal patterns from heterogeneous time-series with irrelevant features for fault event detection

Statistical Analysis and Data Mining

Volumn 2, Issue 1, 2009, Pages 1-17

  Fujimaki, Ryohei ^a   Nakata, Takayuki ^a   Tsukahara, Hidenori ^a   Sato, Akinori ^a   Yamanishi, Kenji ^a  

^a NEC CORPORATION   (Japan)

Author keywords

Fault event detection; Feature selection; Heterogeneous time series

Indexed keywords

FAULT DETECTION; FEATURE SELECTION; ITERATIVE METHODS;

ABNORMAL PATTERNS; EVENTS DETECTION; FAULT EVENT; FAULT EVENT DETECTION; FEATURES SELECTION; HETEROGENEOUS FEATURES; HETEROGENEOUS TIME SERIES; MACHINE SYSTEMS; MULTIVARIATE TIME SERIES; TIMES SERIES;

TIME SERIES;

EID: 69949112755     PISSN: 19321872     EISSN: 19321864     Source Type: Journal    
DOI: 10.1002/sam.10030     Document Type: Article

References (34)

1. R. Fujimaki, T. Nakata, H. Tsukahara, A. Sato, and Y. Yamanishi, Mining abnormal patterns from heterogeneous time-series with irrelevant features for fault event detection, In Proceedings of the SIAM Data Mining Conference, Atlanta, Georgia, 2008.
2. T. Idé and K. Inoue, Knowledge discovery from heterogeneous dynamic systems using change-point correlations, In Proceedings of the 2005 SIAM International Data Mining Conference, Newport Beach, CA, 2005.
3. Y. Li, M. Pont, and N. B. Jones, Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where unknown faults may occur, Pattern Recogn Lett, 23(5) (2002), 569-677.
4. V. Verma, G. Gordon, R. Simmons, and S. Thrun, Particle filters for rover fault diagnosis, IEEE Robotics and Automation Magazine special issue on Human Centered Robotics and Dependability.
5. C. Yang and S. Letourneau, Learning to predict train wheel failures, In Proceeding of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 2005, Chigago, IL, 516-525.
6. U. Lerner, Hybrid Bayesian Networks for Reasoning about Complex Systems. Ph.D. Thesis; Stanford University, 2002.
7. T. Onoda, N. Ito, and H. Yamasaki, One-class svm based unusual condition monitoring for risk management of hydroelectric power plants, In Proceedings of the 2007 International Joint Conference on Neural Networks, Orlando, FL, 2007.
8. R. Fujimaki, T. Yairi, and K. Machida, An approach to spacecraft anomaly detection problem using kernel feature space, In Proceeding of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, Chigago, IL, 2005, 401-410.
9. Y. Kawahara, T. Yairi, and K. Machida, Change-point detection in time series data based on subspace identification, In Proceedings of the 7th IEEE International Conference on Data Mining, Omaha, NE, 2007.
10. J. Ma and S. Perkins, Time-series novelty detection using one-class support vector machines, In Proceedings of international Joint Conference on Neural Networks, Portland, Oregon, 2003.
11. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, Knowledge discovery and data mining: towards a unifying framework, In Proceedings 2nd International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, 1996, 82-88.
12. C. Aggrawal and P. Yu. Outlier detection for high dimensional data, In Proceeding of the 7th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, San Francisco, California, 2001, 37-46.
13. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification (2nd ed.), New York Wiley-Interscience, 2000.
14. M. Chen, A. X. Zheng, J. Lloyd, M. I. Jordan, and E. A. Brewer, Failure diagnosis using decision trees, In Proceedings of the 1st International Conference on Autonomic Computing (ICAC 2004), New York, NY, 2004, 36-43.
15. Y. Li, Y.-Z. Cal, R.-P. Yin, and X.-M. Xu, Fault diagnosis based on support vector machine ensemble, In Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 2005, 3309-3314.
16. Y. Bo-Suk, H. Tian, and Y. Zhong-Jun, Fault diagnosis system of induction motors using feature extraction, feature selection and classification algorithm. JSME Int J Ser C- Mech Syst, Mach Elem Manuf 49(3) (2006), 734-741.
17. K. Wang, Neural network approach to vibration feature selection and multiple fault detection for mechanical systems, In Proceedings of the 1st International Conference on Innovative Computing, Information and Control, Beijing, China, 2006.
18. M. Fink. Object classification from a single example utilizing class relevance metrics, In Advances in Neural Information Processing Systems 17, 2005, 449-456.
19. M. M. Breunig, H. P. Kriegel, R. T. Ng, and J. Sander, Lof: indentifying density based local outliers, In Proceeding of ACM SIGMOD Conference, Dallas, Texas, 2000.
20. A. Lazarevic and V. Kumar. Feature bagging for outlier detection, In Proceeding of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, Chigago, IL, 2005.
21. K. Yamanishi, J. Takeuchi, G. Williams, and P. Milne, Online unsupervised outlier detection using finite mixtures with discounting learning algorithms, Data Min Knowl Discov J 8(3) (2004), 275-300.
22. V. Guralnik and J. Srivastava, Event detection from time series data, In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, 1999, 33-42.
23. X. Xuan and K. Murphy, Modeling changing dependency structure in multivariate time series, In Proceedings of the 24th International Conference on Machine Learning, Corvallis, OR, 2007, 1055-1062.
24. J. Takeuchi and K. Yamanishi, A unifying framework for detecting outliers and change points from time series, IEEE Trans Knowl Data Eng 18(4) (2006), 482-492.
25. V. Chandola, A. Banerjee, and V. Kumar, Anomaly Detection: A Survey, University of Minnesota - Computer Science and Engineering, Technical Report 07-017, 2007.
26. M. Markou and S. Singh, Novelty detection: a review. part 1: statistical approaches, Signal Proces 83 (2003), 2481-2497.
27. M. Markou and S. Singh, Novelty detection: a review. part 2: neural network based approaches. Signal Process 83 (2003), 2499-2521.
28. A. X. Zheng, I. Rish, and A. Beygelzimer, Entropy Approximation for Active Fault Diagnosis, IBM Technical Report RC23441, 2004, http://domino.research.ibm.com/comm/researchpeople.nsf/pages/rish.pubs.html.
29. A. Campoccia, L. Dusonchet, A. Augugliaro, E. R. Sanseverino, and D. Silvestri, Ga-based feature selection for faults identification in electricaldistribution systems, In Proceedings of IEEE International Conference on Electric Power Engineering, Budapest, 1999.
30. N. Abe, B. Zadrozny, and J. Langford, Outlier detection by active learning, In Proceeding of the 12th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, Philadelphia, 2006.
31. M. Wu and C. Jermaine, Outlier detection by sampling with accuracy guarantees, In Proceeding of the 12th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, Philadelphia, 2006.
32. S.Mika,G.R.J.Weston,B.Schölkopf, and K.-R. Müller, Fisher discriminant analysis with kernels, In 1999 IEEE Workshop on Neural Networks for Signal Processing IX, Madison, Wisconsin, 1999.
33. V. Vapnik, Statistical Learning Theory, New york, John Wiley & Sons, 1998.
34. T. Murase, Y. Fukushima, M. Kobayashi, S. Nishimoto, R. Fujimaki, and T. Yokohira. Performance evaluation of a multi-stage network event detection scheme against dos attacks by random scan worms, In Proceedings of IEEE International Communications Quality and Reliability Workshop, Carefree, Arizona, 2008.