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Towards conformal methods for large-scale monitoring of district heating substations
Halmstad University, School of Information Technology.
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: urn:nbn:se:hh:diva-46276ISBN: 978-91-88749-77-2 (print)ISBN: 978-91-88749-78-9 (electronic)OAI: oai:DiVA.org:hh-46276DiVA, id: diva2:1634156
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
List of papers
1. On monitoring heat-pumps with a group-based conformal anomaly detection approach
Open this publication in new window or tab >>On monitoring heat-pumps with a group-based conformal anomaly detection approach
2018 (English)In: ICDATA' 18: Proceedings of the 2018 International Conference on Data Science / [ed] Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr, CSREA Press, 2018, p. 63-69Conference paper, Published paper (Refereed)
Abstract [en]

The ever increasing complexity of modern systems and equipment make the task of monitoring their health quite challenging. Traditional methods such as expert defined thresholds, physics based models and process history based techniques have certain drawbacks. Thresholds defined by experts require deep knowledge about the system and are often too conservative. Physics driven approaches are costly to develop and maintain. Finally, process history based models require large amount of data that may not be available at design time of a system. Moreover, the focus of these traditional approaches has been system specific. Hence, when industrial systems are deployed on a large scale, their monitoring becomes a new challenge. Under these conditions, this paper demonstrates the use of a group-based selfmonitoring approach that learns over time from similar systems subject to similar conditions. The approach is based on conformal anomaly detection coupled with an exchangeability test that uses martingales. This allows setting a threshold value based on sound theoretical justification. A hypothesis test based on this threshold is used to decide on if a system has deviated from its group. We demonstrate the feasibility of this approach through a real case study of monitoring a group of heat-pumps where it can detect a faulty hot-water switch-valve and a broken outdoor temperature sensor without previously observing these faults.

Place, publisher, year, edition, pages
CSREA Press, 2018
Keywords
group-based monitoring, nonconformity measure (NCM), martingale test
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-40961 (URN)1-60132-481-2 (ISBN)9781601324818 (ISBN)
Conference
2018 Internal Conference on Data Science (ICDATA’18), Las Vegas, NV, USA
Available from: 2019-11-16 Created: 2019-11-16 Last updated: 2022-02-01Bibliographically approved
2. Towards understanding district heating substation behavior using robust first difference regression
Open this publication in new window or tab >>Towards understanding district heating substation behavior using robust first difference regression
2018 (English)In: Energy Procedia, Amsterdam: Elsevier, 2018, Vol. 149, p. 236-245Conference paper, Published paper (Refereed)
Abstract [en]

The behavior of a district heating (DH) substation has a social and operational context. The social context comes from its general usage pattern and personal requirements of building inhabitants. The operational context comes from its configuration settings which considers both the weather conditions and social requirements. The parameter estimating thermal energy demand response with respect to change in outdoor temperature conditions along with the strength of the relationship between these variables are two important measures of operational efficiency of a substation. In practice, they can be estimated using a regression model where the slope parameter measures the average response and R2 measures the strength of the relationship. These measures are also important from a monitoring perspective. However, factors related to the social context of a building and the presence of unexplained outliers can make the estimation of these measures a challenging task. Social context of a data point in DH, in many cases appears as an outlier. Data efficiency is also required if these measures are to be estimated in a timely manner. Under these circumstances, methods that can isolate and reduce the effect of outliers in a principled and data efficient manner are required. We therefore propose to use Huber regression, a robust method based on M-estimator type loss function. This method can not only identify possible outliers present in the data of each substation but also reduce their effect on the estimated slope parameter. Moreover, substations that are comparable according to certain criteria, for instance, those with almost identical energy demand levels, should have relatively similar slopes. This provides an opportunity to observe deviating substations under the assumption that comparable substations should show homogeneity in their behavior. Furthermore, the slope parameter can be compared across time to observe if the dynamics of a substation has changed. Our analysis shows that Huber regression in combination with ordinary least squares can provide reliable estimates on the operational efficiency of DH substations. © 2018 The Authors. Published by Elsevier Ltd.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2018
Series
Energy Procedia, E-ISSN 1876-6102 ; 149
Keywords
district heating, energy demand response, outliers, robust regression, substation control, Energy management, Regression analysis, Statistics, Energy demands, Heating substations, Operational efficiencies, Ordinary least squares, Parameter estimating, Robust regressions, Substation controls, Parameter estimation
National Category
Energy Systems
Identifiers
urn:nbn:se:hh:diva-38729 (URN)10.1016/j.egypro.2018.08.188 (DOI)000482873900025 ()2-s2.0-85054085009 (Scopus ID)
Conference
16th International Symposium on District Heating and Cooling, DHC 2018, 9-12 September, 2018
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2022-02-01Bibliographically approved
3. Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach
Open this publication in new window or tab >>Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach
Show others...
2020 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 90, article id 103492Article in journal (Refereed) Published
Abstract [en]

A typical district heating (DH) network consists of hundreds, sometimes thousands, of substations. In the absence of a well-understood prior model or data labels about each substation, the overall monitoring of such large number of substations can be challenging. To overcome the challenge, an approach based on the collective operational monitoring of each substation by a local group (i.e., the reference-group) of other similar substations in the network was formulated. Herein, if a substation of interest (i.e., the target) starts to behave differently in comparison to those in its reference-group, then it was designated as an outlier. The approach was demonstrated on the monitoring of the return temperature variable for atypical and faulty operational behavior in 778 substations associated with multi-dwelling buildings. The choice of an appropriate similarity measure along with its size k were the two important factors that enables a reference-group to effectively detect an outlier target. Thus, different similarity measures and size k for the construction of the reference-groups were investigated, which led to the selection of the Euclidean distance with = 80. This setup resulted in the detection of 77 target substations that were outliers, i.e., the behavior of their return temperature changed in comparison to the majority of those in their respective reference-groups. Of these, 44 were detected due to the local construction of the reference-groups. In addition, six frequent patterns of deviating behavior in the return temperature of the substations were identified using the reference-group based approach, which were then further corroborated by the feedback from a DH domain expert. © 2020 Elsevier Ltd

Place, publisher, year, edition, pages
Oxford: Elsevier, 2020
Keywords
District heating substations, Return temperature, Reference-group based operational monitoring, Fault detection, Outlier detection
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
urn:nbn:se:hh:diva-40962 (URN)10.1016/j.engappai.2020.103492 (DOI)000528194400012 ()2-s2.0-85078822459 (Scopus ID)
Funder
Knowledge Foundation, 20160103
Available from: 2019-11-16 Created: 2019-11-16 Last updated: 2022-02-01Bibliographically approved
4. Mondrian conformal anomaly detection for fault sequence identification in heterogeneous fleets
Open this publication in new window or tab >>Mondrian conformal anomaly detection for fault sequence identification in heterogeneous fleets
2021 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 462, p. 591-606Article in journal (Refereed) Published
Abstract [en]

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. © 2021

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2021
Keywords
Conformal anomaly detection, Conformal anomaly sequence (CAS), District heating, Sub-fleet based monitoring, Substation monitoring
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-45735 (URN)10.1016/j.neucom.2021.08.016 (DOI)000696933600013 ()2-s2.0-85115660223 (Scopus ID)
Available from: 2021-10-15 Created: 2021-10-15 Last updated: 2022-02-01Bibliographically approved
5. A conformal anomaly detection based industrial fleet monitoring framework: A case study in district heating
Open this publication in new window or tab >>A conformal anomaly detection based industrial fleet monitoring framework: A case study in district heating
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
Keywords
Conformal anomaly detection, Fleet monitoring, Unit-level model, Subfleet-level model, Ensemble model, District heating substations
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-46273 (URN)10.1016/j.eswa.2022.116864 (DOI)000798741200007 ()2-s2.0-85129522080 (Scopus ID)
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

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