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research-article

Mondrian conformal anomaly detection for fault sequence identification in heterogeneous fleets

Authors:

Stefan Byttner,

Mohamed-Rafik Bouguelia,

Henrik GaddAuthors Info & Claims

Volume 462, Issue C

Pages 591 - 606

https://doi.org/10.1016/j.neucom.2021.08.016

Published: 28 October 2021 Publication History

Abstract

We considered the case of monitoring a large fleet where heterogeneity in the operational behavior among its constituent units (i.e., systems or machines) is non-negligible, and no labeled data is available. Each unit in the fleet, referred to as a target, is tracked by its sub-fleet. A conformal sub-fleet (CSF) is a set of units that act as a proxy for the normal operational behavior of a target unit by relying on the Mondrian conformal anomaly detection framework. Two approaches, the k-nearest neighbors and conformal clustering, were investigated for constructing such a sub-fleet by formulating a stability criterion. Moreover, it is important to discover the sub-sequence of events that describes an anomalous behavior in a target unit. Hence, we proposed to extract such sub-sequences for further investigation without pre-specifying their length. We refer to it as a conformal anomaly sequence (CAS). Furthermore, different nonconformity measures were evaluated for their efficiency, i.e., their ability to detect anomalous behavior in a target unit, based on the length of the observed CAS and the S-criterion value. The CSF approach was evaluated in the context of monitoring district heating substations. Anomalous behavior sub-sequences were corroborated with the domain expert leading to the conclusion that the proposed approach has the potential to be useful for both diagnostic and knowledge extraction purposes, especially in domains where labeled data is not available or hard to obtain.

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Cited By

Farouq SByttner SBouguelia MGadd H(2022)A conformal anomaly detection based industrial fleet monitoring frameworkExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116864201:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116864

Index Terms

Mondrian conformal anomaly detection for fault sequence identification in heterogeneous fleets
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Neurocomputing

Neurocomputing Volume 462, Issue C

Oct 2021

608 pages

ISSN:0925-2312

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Copyright © 2021.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 28 October 2021

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Cited By

Farouq SByttner SBouguelia MGadd H(2022)A conformal anomaly detection based industrial fleet monitoring frameworkExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116864201:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116864

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