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Calikus, E., Nowaczyk, S., Pinheiro Sant'Anna, A. & Dikmen, O. (2020). No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection. Expert systems with applications
Open this publication in new window or tab >>No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection
2020 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793Article in journal (Refereed) Submitted
Abstract [en]

In recent years, there has been increased research interest in detecting anomalies in temporal streaming data. A variety of algorithms have been developed in the data mining community, which can be divided into two categories (i.e., general and ad hoc). In most cases, general approaches assume the one-size-fits-all solution model where a single anomaly detector can detect all anomalies in any domain.  To date, there exists no single general method that has been shown to outperform the others across different anomaly types, use cases and datasets. On the other hand, ad hoc approaches that are designed for a specific application lack flexibility. Adapting an existing algorithm is not straightforward if the specific constraints or requirements for the existing task change. In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection, which provides a flexible and extensible anomaly detection procedure. SAFARI helps to facilitate more elaborate algorithm comparisons by allowing us to isolate the effects of shared and unique characteristics of different algorithms on detection performance. Using SAFARI, we have implemented various anomaly detectors and identified a research gap that motivates us to propose a novel learning strategy in this work. We conducted an extensive evaluation study of 20 detectors that are composed using SAFARI and compared their performances using real-world benchmark datasets with different properties. The results indicate that there is no single superior detector that works well for every case, proving our hypothesis that "there is no free lunch" in the streaming anomaly detection world. Finally, we discuss the benefits and drawbacks of each method in-depth and draw a set of conclusions to guide future users of SAFARI.

Place, publisher, year, edition, pages
Oxford: Elsevier, 2020
Keywords
anomaly detection
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-41420 (URN)
Funder
Knowledge Foundation, 20160103
Available from: 2020-01-29 Created: 2020-01-29 Last updated: 2020-02-18
Calikus, E. (2020). Self-Monitoring using Joint Human-Machine Learning: Algorithms and Applications. (Licentiate dissertation). Halmstad: Halmstad University Press
Open this publication in new window or tab >>Self-Monitoring using Joint Human-Machine Learning: Algorithms and Applications
2020 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The ability to diagnose deviations and predict faults effectively is an important task in various industrial domains for minimizing costs and productivity loss and also conserving environmental resources. However, the majority of the efforts for diagnostics are still carried out by human experts in a time-consuming and expensive manner. Automated data-driven solutions are needed for continuous monitoring of complex systems over time. On the other hand, domain expertise plays a significant role in developing, evaluating, and improving diagnostics and monitoring functions. Therefore, automatically derived solutions must be able to interact with domain experts by taking advantage of available a priori knowledge and by incorporating their feedback into the learning process.

This thesis and appended papers tackle the problem of generating a real-world self-monitoring system for continuous monitoring of machines and operations by developing algorithms that can learn data streams and their relations over time and detect anomalies using joint-human machine learning. Throughout this thesis, we have described a number of different approaches, each designed for the needs of a self-monitoring system, and have composed these methods into a coherent framework. More specifically, we presented a two-layer meta-framework, in which the first layer was concerned with learning appropriate data representations and detectinganomalies in an unsupervised fashion, and the second layer aimed at interactively exploiting available expert knowledge in a joint human-machine learning fashion.

Furthermore, district heating has been the focus of this thesis as the application domain with the goal of automatically detecting faults and anomalies by comparing heat demands among different groups of customers. We applied and enriched different methods on this domain, which then contributed to the development and improvement of the meta-framework. The contributions that result from the studies included in this work can be summarized into four categories: (1) exploring different data representations that are suitable for the self-monitoring task based on data characteristics and domain knowledge, (2) discovering patterns and groups in data that describe normal behavior of the monitored system/systems, (3) implementing methods to successfully discriminate anomalies from the normal behavior, and (4) incorporating domain knowledge and expert feedback into self-monitoring.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2020. p. 45
Series
Halmstad University Dissertations ; 69
Keywords
self-monitoring, anomaly detection, machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-41421 (URN)978-91-88749-47-5 (ISBN)978-91-88749-46-8 (ISBN)
Presentation
2020-02-25, J102 Wigforssalen, Kristian IV:s väg 3, Halmstad, 13:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation, 20160103
Available from: 2020-01-31 Created: 2020-01-29 Last updated: 2020-01-31Bibliographically approved
Calikus, E., Nowaczyk, S., Pinheiro Sant'Anna, A., Gadd, H. & Werner, S. (2019). A data-driven approach for discovering heat load patterns in district heating. Applied Energy, 252, Article ID 113409.
Open this publication in new window or tab >>A data-driven approach for discovering heat load patterns in district heating
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2019 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 252, article id 113409Article in journal (Refereed) Published
Abstract [en]

Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are limited to small-scale analyses that are not representative enough to understand the behavior of the overall network. In this work, we propose a data-driven approach that enables large-scale automatic analysis of heat load patterns in district heating networks without requiring prior knowledge. Our method clusters the customer profiles into different groups, extracts their representative patterns, and detects unusual customers whose profiles deviate significantly from the rest of their group. Using our approach, we present the first large-scale, comprehensive analysis of the heat load patterns by conducting a case study on many buildings in six different customer categories connected to two district heating networks in the south of Sweden. The 1222 buildings had a total floor space of 3.4 million square meters and used 1540 TJ heat during 2016. The results show that the proposed method has a high potential to be deployed and used in practice to analyze and understand customers’ heat-use habits. © 2019 Calikus et al. Published by Elsevier Ltd.

Place, publisher, year, edition, pages
Oxford: Elsevier, 2019
Keywords
District heating, Energy efficiency, Heat load patterns, Clustering, Abnormal heat use
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-40907 (URN)10.1016/j.apenergy.2019.113409 (DOI)000497968000013 ()2-s2.0-85066961984 (Scopus ID)
Funder
Knowledge Foundation, 20160103
Available from: 2019-11-12 Created: 2019-11-12 Last updated: 2020-04-22Bibliographically approved
Calikus, E., Fan, Y., Nowaczyk, S. & Pinheiro Sant'Anna, A. (2019). Interactive-cosmo: Consensus self-organized models for fault detection with expert feedback. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, Australia; 15 February, 2019 (pp. 1-9). New York: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Interactive-cosmo: Consensus self-organized models for fault detection with expert feedback
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, New York: Association for Computing Machinery (ACM), 2019, p. 1-9Conference paper, Published paper (Refereed)
Abstract [en]

Diagnosing deviations and predicting faults is an important task, especially given recent advances related to Internet of Things. However, the majority of the efforts for diagnostics are still carried out by human experts in a time-consuming and expensive manner. One promising approach towards self-monitoring systems is based on the "wisdom of the crowd" idea, where malfunctioning equipments are detected by understanding the similarities and differences in the operation of several alike systems.

A fully autonomous fault detection, however, is not possible, since not all deviations or anomalies correspond to faulty behaviors; many can be explained by atypical usage or varying external conditions. In this work, we propose a method which gradually incorporates expert-provided feedback for more accurate self-monitoring. Our idea is to support model adaptation while allowing human feedback to persist over changes in data distribution, such as concept drift. © 2019 Association for Computing Machinery.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2019
Keywords
Anomaly Detection, Self-Monitoring, Active Learning, Human-in- the-loop
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-41365 (URN)10.1145/3304079.3310289 (DOI)2-s2.0-85069779014 (Scopus ID)978-1-4503-6296-2 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, Australia; 15 February, 2019
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-01-30Bibliographically approved
Nowaczyk, S., Pinheiro Sant'Anna, A., Calikus, E. & Fan, Y. (2018). Monitoring equipment operation through model and event discovery. In: Hujun Yin, David Camacho Paulo Novais & Antonio J. Tallón-Ballesteros (Ed.), Intelligent Data Engineering and Automated Learning – IDEAL 2018: 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II. Paper presented at Intelligent Data Engineering and Automated Learning – IDEAL 2018, 19th International Conference, Madrid, Spain, November 21–23, 2018 (pp. 41-53). Cham: Springer, 11315
Open this publication in new window or tab >>Monitoring equipment operation through model and event discovery
2018 (English)In: Intelligent Data Engineering and Automated Learning – IDEAL 2018: 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II / [ed] Hujun Yin, David Camacho Paulo Novais & Antonio J. Tallón-Ballesteros, Cham: Springer, 2018, Vol. 11315, p. 41-53Conference paper, Published paper (Refereed)
Abstract [en]

Monitoring the operation of complex systems in real-time is becoming both required and enabled by current IoT solutions. Predicting faults and optimising productivity requires autonomous methods that work without extensive human supervision. One way to automatically detect deviating operation is to identify groups of peers, or similar systems, and evaluate how well each individual conforms with the group. We propose a monitoring approach that can construct knowledge more autonomously and relies on human experts to a lesser degree: without requiring the designer to think of all possible faults beforehand; able to do the best possible with signals that are already available, without the need for dedicated new sensors; scaling up to “one more system and component” and multiple variants; and finally, one that will adapt to changes over time and remain relevant throughout the lifetime of the system. © Springer Nature Switzerland AG 2018.

Place, publisher, year, edition, pages
Cham: Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11315
Keywords
Artificial intelligence, Computer science, Computers, Event discoveries, Human expert, Human supervision, Monitoring approach, Monitoring equipment, Multiple variants, Real time, Scaling-up, Real time systems
National Category
Embedded Systems
Identifiers
urn:nbn:se:hh:diva-38732 (URN)10.1007/978-3-030-03496-2_6 (DOI)2-s2.0-85057087564 (Scopus ID)9783030034955 (ISBN)978-3-030-03496-2 (ISBN)
Conference
Intelligent Data Engineering and Automated Learning – IDEAL 2018, 19th International Conference, Madrid, Spain, November 21–23, 2018
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-01-08Bibliographically approved
Calikus, E., Nowaczyk, S., Pinheiro Sant'Anna, A. & Byttner, S. (2018). Ranking Abnormal Substations by Power Signature Dispersion. Paper presented at 16th International Symposium on District Heating and Cooling, DHC2018, Hamburg, Germany, 9-12 September, 2018. Energy Procedia, 149, 345-353
Open this publication in new window or tab >>Ranking Abnormal Substations by Power Signature Dispersion
2018 (English)In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 149, p. 345-353Article in journal (Refereed) Published
Abstract [en]

The relation between heat demand and outdoor temperature (heat power signature) is a typical feature used to diagnose abnormal heat demand. Prior work is mainly based on setting thresholds, either statistically or manually, in order to identify outliers in the power signature. However, setting the correct threshold is a difficult task since heat demand is unique for each building. Too loose thresholds may allow outliers to go unspotted, while too tight thresholds can cause too many false alarms.

Moreover, just the number of outliers does not reflect the dispersion level in the power signature. However, high dispersion is often caused by fault or configuration problems and should be considered while modeling abnormal heat demand.

In this work, we present a novel method for ranking substations by measuring both dispersion and outliers in the power signature. We use robust regression to estimate a linear regression model. Observations that fall outside of the threshold in this model are considered outliers. Dispersion is measured using coefficient of determination R2 which is a statistical measure of how close the data are to the fitted regression line.

Our method first produces two different lists by ranking substations using number of outliers and dispersion separately. Then, we merge the two lists into one using the Borda Count method. Substations appearing on the top of the list should indicate higher abnormality in heat demand compared to the ones on the bottom. We have applied our model on data from substations connected to two district heating networks in the south of Sweden. Three different approaches i.e. outlier-based, dispersion-based and aggregated methods are compared against the rankings based on return temperatures. The results show that our method significantly outperforms the state-of-the-art outlier-based method. © 2018 The Authors. Published by Elsevier Ltd.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2018
Keywords
abnormal heat demand, district heating, anomaly detection, fault detection, power signature
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-38253 (URN)10.1016/j.egypro.2018.08.198 (DOI)000482873900036 ()2-s2.0-85054100441 (Scopus ID)
Conference
16th International Symposium on District Heating and Cooling, DHC2018, Hamburg, Germany, 9-12 September, 2018
Funder
Knowledge Foundation, 20160103
Available from: 2018-11-04 Created: 2018-11-04 Last updated: 2020-02-03Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6249-4144

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