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A conformal anomaly detection based industrial fleet monitoring framework: A case study in district heating
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-2859-6155
Halmstad University, School of Business, Innovation and Sustainability.
2022 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 201, article id 116864Article in journal (Refereed) Published
Abstract [en]

The monitoring infrastructure of an industrial fleet can rely on the so-called unit-level and subfleet-level models to observe the behavior of a target unit. However, such infrastructure has to confront several challenges. First, from an anomaly detection perspective of monitoring a target unit, unit-level and subfleet-level models can give different information about the nature of an anomaly, and which approach or level model is appropriate is not always clear. Second, in the absence of well-understood prior models of unit and subfleet behavior, the choice of a base model at their respective levels, especially in an online/streaming setting, may not be clear. Third, managing false alarms is a major problem. To deal with these challenges, we proposed to rely on the conformal anomaly detection framework. In addition, an ensemble approach was deployed to mitigate the knowledge gap in understanding the underlying data-generating process at the unit and subfleet levels. Therefore, to monitor the behavior of a target unit, a unit-level ensemble model (ULEM) and a subfleet-level ensemble model (SLEM) were constructed, where each member of the respective ensemble is based on a conformal anomaly detector (CAD). However, since the information obtained by these two ensemble models through their p-values may not always agree, a combined ensemble model (CEM) was proposed. The results are based on real-world operational data obtained from district heating (DH) substations. Here, it was observed that CEM reduces the overall false alarms compared to ULEM or SLEM, albeit at the cost of some detection delay. The analysis demonstrated the advantages and limitations of ULEM, SLEM, and CEM. Furthermore, discords obtained from the state-of-the-art matrix-profile (MP) method and the combined calibration scores obtained from ULEM and SLEM were compared in an offline setting. Here, it was observed that SLEM achieved a better overall precision and detection delay. Finally, the different components related to ULEM, SLEM, and CEM were put together into what we refer to as TRANTOR: a conformal anomaly detection based industrial fleet monitoring framework. The proposed framework is expected to enable fleet operators in various domains to improve their monitoring infrastructure by efficiently detecting anomalous behavior and controlling false alarms at the target units. © 2022

Place, publisher, year, edition, pages
Oxford: Elsevier, 2022. Vol. 201, article id 116864
Keywords [en]
Conformal anomaly detection, Fleet monitoring, Unit-level model, Subfleet-level model, Ensemble model, District heating substations
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-46273DOI: 10.1016/j.eswa.2022.116864ISI: 000798741200007Scopus ID: 2-s2.0-85129522080OAI: oai:DiVA.org:hh-46273DiVA, id: diva2:1634134
Funder
Knowledge Foundation, 20160103
Note

Som manuskript i avhandling / As manuscript in thesis

Available from: 2022-02-01 Created: 2022-02-01 Last updated: 2022-09-01Bibliographically approved
In thesis
1. Towards conformal methods for large-scale monitoring of district heating substations
Open this publication in new window or tab >>Towards conformal methods for large-scale monitoring of district heating substations
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Increasing technical complexity, design variations, and customization options of IoT units create difficulties for the construction of monitoring infrastructure. These units can be associated with different domains, such as a fleet of vehicles in the mobility domain and a fleet of heat-pumps in the heating domain. The lack of labeled datasets and well-understood prior unit and fleet behavior models exacerbates the problem. Moreover, the time-series nature of the data makes it difficult to strike a reasonable balance between precision and detection delay. The thesis aims to develop a framework for scalable and cost-efficient monitoring of industrial fleets. The investigations were conducted on real-world operational data obtained from District Heating (DH) substations to detect anomalous behavior and faults. A foundational hypothesis of the thesis is that fleet-level models can mitigate the lack of labeled datasets, improve anomaly detection performance, and achieve a scalable monitoring alternative.

Our preliminary investigations found that operational heterogeneity among the substations in a DH network can cause fleet-level models to be inefficient in detecting anomalous behavior at the target units. An alternative is to rely on subfleet-level models to act as a proxy for the behavior of target units. However, the main difficulty in constructing a subfleet-level model is the selection of its members such that their behavior is stable over time and representative of the target unit. Therefore, we investigated various ways of constructing the subfleets and estimating their stability. To mitigate the lack of well-understood prior unit and fleet behavior models, we proposed constructing Unit-Level and Subfleet-Level Ensemble Models, i.e., ULEM and SLEM. Herein, each member of the respective ensemble consists of a Conformal Anomaly Detector (CAD). Each ensemble yields a nonconformity score matrix that provides information about the behavior of a target unit relative to its historical data and its subfleet, respectively. However, these ensemble models can give different information about the nature of an anomaly that may not always agree with each other. Therefore, we further synthesized this information by proposing a Combined Ensemble Model (CEM). We investigated the advantages and limitations of decisions that rely on the information obtained from ULEM, SLEM, and CEM using precision and detection delay. We observed the decisions that relied on the information obtained through CEM showed a reduction in overall false alarms compared to those obtained through ULEM or SLEM, albeit at the cost of some detection delay. Finally, we combined the components of ULEM, SLEM, and CEM into what we refer to as TRANTOR: a conformal anomaly detection based indusTRiAl fleet moNiTORing framework. The proposed framework is expected to enable fleet operators in various domains to improve their monitoring infrastructure by efficiently detecting anomalous behavior and controlling false alarms at the target units.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2022. p. 98
Series
Halmstad University Dissertations ; 84
National Category
Computer and Information Sciences Computer Sciences
Identifiers
urn:nbn:se:hh:diva-46276 (URN)978-91-88749-77-2 (ISBN)978-91-88749-78-9 (ISBN)
Public defence
2022-03-08, Wigforss (J102), Visionen, Halmstad, 13:00 (English)
Opponent
Supervisors
Available from: 2022-02-07 Created: 2022-02-01 Last updated: 2022-04-27Bibliographically approved

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Farouq, ShirazByttner, StefanBouguelia, Mohamed-RafikGadd, Henrik

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