Time discretisation applied to anomaly detection in a marine engine

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4692 LNAI, Issue PART 1, 2007, Pages 405-412

  Morgan, Ian ^a   Liu, Honghai ^a   Turnbull, George ^a   Brown, David ^a  

^a UNIVERSITY OF PORTSMOUTH   (United Kingdom)

Author keywords

Anomaly detection; Discretisation; Symbolic aggregate approximation; Time series

Indexed keywords

COST BENEFIT ANALYSIS; TIME SERIES ANALYSIS;

ANOMALY DETECTION; DISCRETISATION; SYMBOLIC AGGREGATE APPROXIMATION; TIME SERIES;

INTRUSION DETECTION;

EID: 38149037923     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74819-9_50     Document Type: Conference Paper

References (19)

1. Dragsted, J., Bergeson, O.: Influence of low cylinder consumption on operating cost for 2-stroke engines. International council on combustion engines, CIMAC Congress, Kyoto 9 (2004)
2. Wilson, S.: System functional specification for elemental analysis system. Technical report. Rivertek Industrial Ltd (2005)
3. Palade, V., Patton, R., Uppal, F., Quevedo, J., Daley, S.: Fault diagnosis of an industrial gas turbine using neuro-fuzzy methods. In: Proceedings of the 15th IFAC World Congress, pp. 2477-2482 (2002)
4. Yan, W., Goebel, K., Li, C.: Flight regime mapping for aircraft engine fault diagnosis. In: Proceedings of the 58th Meeting of the Society of Mechanical Failures Prevention Technology, pp. 153-164 (2004)
5. Macian, V., Tormos, B., Sala, A., Ramirez, J.: Fuzzy logic-based expert system for diesel engine oil analysis diagnosis. Insight 8, 1-8 (2006)
6. Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems 3(3), 263-286 (2001)
7. Keogh, E., Lonardi, S., Chiu, B.: Finding surprising patterns in a time series database in linear time and space. In: Proceedings of ACM Knowledge Discovery and Data Mining, pp. 550-556 (2002)
8. Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. ACM Workshop on Research Issues in Data Mining and Knowledge Discovery, 2-11 (2003)
9. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines, Software (2001), available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
10. Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases, pp. 69-84 (1993)
11. Chan, K.p., Fu, A.W.-C: Efficient time series matching by wavelets. ICDE (1999)
12. Lkhagva, B., Suzuki, Y., Kawagoe, K.: New time series data representation ESAX for financial applications. ICDE Workshops (2006)
13. Manevitz, L., Yousef, M.: One-class svms for document classification. Journal of Machine Learning Research 2, 139-154 (2001)
14. Lovell, B., Walder, C.: Support vector machines for business applications. Business Applications and Computational Intelligence, 267-290 (2006)
15. Suykens, A., Gestel, T., Brabanter, J., Moor, B., Vandewalle, J.: Least Squares Support Vector Machines, K.U. Leuven, Belgium (2002)
16. Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines and other Kernel-based learning methods. Cambridge University Press, Cambridge (2000)
17. Burges, C.: A tutorial on support vector machines. Knowledge Discovery and Data Mining, 1-43 (1998)
18. Largeron-Leteno, C.: Prediction suffix trees for supervised classification of sequences. Pattern Recognition Letters 24, 3153-3164 (2003)
19. Altun, Y., Tsochantaridis, I., Hofmann, T.: Hidden markov support vector machines. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML) (2003)