Abstract
We focus on the problem of selecting the few vehicles in a fleet that are expected to last the longest without failure. The prediction of each vehicle’s remaining life is based on the aggregation of estimates from ‘peer’ units, i.e. units with similar design, maintenance, and utilization characteristics. Peers are analogous to neighbors in Case-Based Reasoning, except that the states of the peer units are constantly changing with time and usage. We use an evolutionary learning framework to update the similarity criteria for peer identification. Results indicate that learning from peers is a robust and promising approach for the usually data-poor domain of equipment prognostics. The results also highlight the need for model maintenance to keep such a reasoning system vital over time.
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References
Aggour, K.S., Pavese, M., Bonissone, P.P., Cheetham, W.E.: SOFT-CBR: A Self-Optimizing Fuzzy Tool for Case-Based Reasoning. In: Proceedings of the 5th International Conference on Case-Based Reasoning, pp. 5–19. Springer, Heidelberg (2003)
Berndt, D.J., Clifford, J.: Finding patterns in time series: A dynamic programming approach. In: Advances in Knowledge Discovery and Data Mining, American Association for Artificial Intelligence, pp. 229–248 (1996)
Fritsche, L., Schlaefer, A., Budde, K., Schröter, K., Neumayer, H.H.: Recognition of Critical Situations from Time Series of Laboratory Results by Case-Based Reasoning. Journal of the American Medical Informatics Association 9(5), 520–528 (2002)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Amsterdam (1989)
Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)
Jaczynski, M.: A Framework for the Management of Past Experiences with Time-Extended Situations. In: Proceedings of the 6th International Conference on Information and Knowledge Management, pp. 32–39. ACM Press, New York (1997)
Jang, R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions Systems, Man, and Cybernetics 23(3), 665–685 (1993)
Leake, D.B., Wilson, D.C.: Categorizing Case-Base Maintenance: Dimensions and Directions. In: Proceedings of the 4th European Workshop on Case-Based Reasoning, pp. 196–207. Springer, Heidelberg (1998)
Leake, D.B., Wilson, D.C.: When Experience is Wrong: Examining CBR for Changing Tasks and Environments. In: Proceedings of the 3rd International Conference on Case-Based Reasoning, pp. 218–232. Springer, Heidelberg (1999)
Ram, A., Santamaria, J.C.: Continuous Case-Based Reasoning. Artificial Intelligence 90, 25–77 (1997)
Schlaefer, A., Schröter, K., Fritsche, L.: A Case-Based Approach for the Classification of Medical Time Series. In: Proceedings of the 2nd International Symposium on Medical Data Analysis, pp. 258–263 (2001)
Smyth, B.: Case-Base Maintenance. In: Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, vol. 2, pp. 507–516 (1998)
Varma, A., Roddy, N.: ICARUS: design and deployment of a case-based reasoning system for locomotive diagnostics. Engineering Applications of Artificial Intelligence 12(6), 681–690 (1999)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, San Francisco (2000)
Zhang, Z., Yang, Q.: Towards Lifetime Maintenance of Case Base Indexes for Continual Case Based Reasoning. In: Proceedings of the 8th International Conference on AI Methodologies, Systems and Applications, pp. 489–500 (1998)
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Varma, A., Aggour, K.S., Bonissone, P.P. (2005). Selecting the Best Units in a Fleet: Performance Prediction from Equipment Peers. In: Muñoz-Ávila, H., Ricci, F. (eds) Case-Based Reasoning Research and Development. ICCBR 2005. Lecture Notes in Computer Science(), vol 3620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536406_45
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DOI: https://doi.org/10.1007/11536406_45
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