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Fault Diagnosis via Fusion of Information from a Case Stream

Authors:

Tomas Olsson, Ning Xiong, Elisabeth Källström , Peter Funk

Research groups:

Artificial Intelligence and Intelligent Systems
Learning and Optimisation

Publication Type:

Conference/Workshop Paper

Venue:

23rd International Conference on Case-Based Reasoning

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. In the first step of the approach, 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 with a sliding window approach. When the stream anomaly detector identifies an anomalous window, the anomalous cases in the window are classified using a CBR approach. Thereafter, the individual classifications are aggregated into a single prediction using an information fusion approach. 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.

Bibtex

@inproceedings{Olsson3962,
author = {Tomas Olsson and Ning Xiong and Elisabeth K{\"a}llstr{\"o}m and Peter Funk},
title = {Fault Diagnosis via Fusion of Information from a Case Stream},
month = {September},
year = {2015},
booktitle = {23rd International Conference on Case-Based Reasoning},
url = {http://www.es.mdu.se/publications/3962-}
}