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  • 1.
    Farouq, Shiraz
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Towards large-scale monitoring of operationally diverse thermal energy systems with data-driven techniques2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The core of many typical large-scale industrial infrastructure consists of hundreds or thousands of systems that are similar in their basic design and purpose. For instance, District Heating (DH) utilities rely on a large network of substations to deliver heat to their customers. Similarly, a factory may require a large fleet of specialized robots for manufacturing a certain product. Monitoring these systems is important for maintaining the overall efficiency of industrial operations by detecting various problems due to faults and misconfiguration. However, this can be challenging since a well-understood prior model for each system is rarely available. In most cases, each system in a fleet or network is fitted with a set of sensors to measure its state at different time intervals. Typically, a data-driven model for each system can be used for their monitoring. However, not all factors that can possibly influence the operations of each system in a fleet or network has an associated sensor. Moreover, sufficient instances of normal, atypical and faulty behavior are rarely available to train such a model. These issues can impede the effectiveness of a system level data-driven model. Alternatively, it can be assumed that since all the systems in a fleet or network are working on a similar task, they should all behave in a homogeneous manner. Any system that behaves differently from the majority is then considered as an outlier. This is referred to as the global model at the fleet or network level. While the approach is simple, it is less effective in the presence of non-stationary working conditions. Hence, both system level and global modeling approaches have their limitations. 

    This thesis investigates system level and fleet or network level (global) models for large-scale monitoring, and proposes an alternative way which is referred to as a reference-group based approach. Herein, the operational monitoring of each system, referred to as a target, is delegated to a reference-group, which consists of systems experiencing a comparable operating regime along with the target. Thus, the definition of a normal, atypical or faulty operational behavior in a target system is described relative to its reference-group. In this sense, if the target system is not behaving operationally in consort with the systems in its reference-group, then it can be inferred that this is either due to a fault or because of some atypical operation arising at the target system due to its local peculiarities. The application area for these investigations is the large-scale operational monitoring of thermal energy systems: networks of district heating (DH) substations and fleets of heatpumps. The current findings indicate three advantages of a reference-group based approach. The first is that the reference operational behavior of any system in the fleet or network does not need to be predefined. The second is that it provides a basis for what a system’s operational behavior should have been and what it is. In this respect, each system in the reference-group provides an evidence about a particular behavior during a particular time period. This can be very useful when the description of a normal, atypical and faulty operational behavior is not available. The third is that it can detect potential atypical and faulty operational behavior quicker compared to global models of outlier detection at the fleet or network level.

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  • 2.
    Farouq, Shiraz
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bouguelia, Mohamed-Rafik
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    On monitoring heat-pumps with a group-based conformal anomaly detection approach2018In: 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 (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.

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  • 3.
    Farouq, Shiraz
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bouguelia, Mohamed-Rafik
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nord, Natasa
    Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
    Gadd, Henrik
    Öresundskraft, Helsingborg, Sweden.
    Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach2020In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 90, article id 103492Article in journal (Refereed)
    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

  • 4.
    Farouq, Shiraz
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gadd, Henrik
    Öresundskraft AB, Ängelholm, Sweden.
    Towards understanding district heating substation behavior using robust first difference regression2018In: Energy Procedia, Amsterdam: Elsevier, 2018, Vol. 149, p. 236-245Conference 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.

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