A one-class Clustering technique for Novelty Detection and Isolation in sensor networks

Maleki, Sepehr and Bingham, Chris (2017) A one-class Clustering technique for Novelty Detection and Isolation in sensor networks. In: 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA).

Full content URL: https://doi.org/10.1109/CIVEMSA.2017.7995292

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

A new Cluster-based methodology for real-time Novelty Detection and Isolation (NDI) in sensor networks, is presented. The proposed algorithm enables uniform clustering across time-frames to indicate the presence of a “healthy” network. In the event of novelty, the associated sensor is seen to be clustered in a non-uniform manner with respect other sensors in the network, thereby facilitating fault isolation. Moreover, a statistical approach is proposed to determine a noise tolerance level for reducing false alarms. Performance of the proposed algorithm is examined using datasets obtained from a number of industrial case studies, and the significance for fault detection for such systems is demonstrated. Specifically, it is shown that through a correct selection of the noise tolerance level, an emerging failure is successfully isolated in presence of other abrupt changes that visually might be perceived as indication of a failure.

Keywords:sensor networks, AI, Machine Learning, Clustering
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science
ID Code:44700
Deposited On:09 Jun 2021 12:37

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