Abstract
This paper presents a novel approach to fault diagnosis applied to a stream of cases. The approach uses a combination of case-based reasoning and information fusion to do classification. The approach consists of two steps. First, we perform local anomaly detection on-board a machine to identify anomalous individual cases. Then, we monitor the stream of anomalous cases using a stream anomaly detector based on a sliding window approach. When the stream anomaly detector identifies an anomalous window, the anomalous cases in the window are classified using a CBR classifier. Thereafter, the individual classifications are aggregated into a composite case with a single prediction using a information fusion method. We compare three information fusion approaches: simple majority vote, weighted majority vote and Dempster-Shafer fusion. As baseline for comparison, we use the classification of the last identified anomalous case in the window as the aggregated prediction.
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Notes
- 1.
Thanks to NASA for allowing us to use the Shuttle data sets.
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Acknowledgements
This work has been partially supported by the FP7 EU Large scale Integrating Project SMART VORTEX co-financed by the European Union [38], the Swedish Knowledge Foundation (KK-stiftelsen) [39] through ITS-EASY Research School and Swedish Governmental Agency for Innovation Systems (VINNOVA) grant no 10020, grant no 2012- 01277 and JU grant no 100266.
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Olsson, T., Xiong, N., Källström, E., Holst, A., Funk, P. (2015). Fault Diagnosis via Fusion of Information from a Case Stream. In: Hüllermeier, E., Minor, M. (eds) Case-Based Reasoning Research and Development. ICCBR 2015. Lecture Notes in Computer Science(), vol 9343. Springer, Cham. https://doi.org/10.1007/978-3-319-24586-7_19
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