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Interactive-cosmo: Consensus self-organized models for fault detection with expert feedback
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-6249-4144
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-3034-6630
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-7796-5201
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-3495-2961
2019 (Engelska)Ingår i: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, New York: Association for Computing Machinery (ACM), 2019, s. 1-9Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
New York: Association for Computing Machinery (ACM), 2019. s. 1-9
Nyckelord [en]
Anomaly Detection, Self-Monitoring, Active Learning, Human-in- the-loop
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
URN: urn:nbn:se:hh:diva-41365DOI: 10.1145/3304079.3310289Scopus ID: 2-s2.0-85069779014ISBN: 978-1-4503-6296-2 (tryckt)OAI: oai:DiVA.org:hh-41365DiVA, id: diva2:1384810
Konferens
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
Tillgänglig från: 2020-01-10 Skapad: 2020-01-10 Senast uppdaterad: 2020-01-30Bibliografiskt granskad
Ingår i avhandling
1. Wisdom of the Crowd for Fault Detection and Prognosis
Öppna denna publikation i ny flik eller fönster >>Wisdom of the Crowd for Fault Detection and Prognosis
2020 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Monitoring and maintaining the equipment to ensure its reliability and availability is vital to industrial operations. With the rapid development and growth of interconnected devices, the Internet of Things promotes digitization of industrial assets, to be sensed and controlled across existing networks, enabling access to a vast amount of sensor data that can be used for condition monitoring. However, the traditional way of gaining knowledge and wisdom, by the expert, for designing condition monitoring methods is unfeasible for fully utilizing and digesting this enormous amount of information. It does not scale well to complex systems with a huge amount of components and subsystems. Therefore, a more automated approach that relies on human experts to a lesser degree, being capable of discovering interesting patterns, generating models for estimating the health status of the equipment, supporting maintenance scheduling, and can scale up to many equipment and its subsystems, will provide great benefits for the industry. 

This thesis demonstrates how to utilize the concept of "Wisdom of the Crowd", i.e. a group of similar individuals, for fault detection and prognosis. The approach is built based on an unsupervised deviation detection method, Consensus Self-Organizing Models (COSMO). The method assumes that the majority of a crowd is healthy; individual deviates from the majority are considered as potentially faulty. The COSMO method encodes sensor data into models, and the distances between individual samples and the crowd are measured in the model space. This information, regarding how different an individual performs compared to its peers, is utilized as an indicator for estimating the health status of the equipment. The generality of the COSMO method is demonstrated with three condition monitoring case studies: i) fault detection and failure prediction for a commercial fleet of city buses, ii) prognosis for a fleet of turbofan engines and iii) finding cracks in metallic material. In addition, the flexibility of the COSMO method is demonstrated with: i) being capable of incorporating domain knowledge on specializing relevant expert features; ii) able to detect multiple types of faults with a generic data- representation, i.e. Echo State Network; iii) incorporating expert feedback on adapting reference group candidate under an active learning setting. Last but not least, this thesis demonstrated that the remaining useful life of the equipment can be estimated from the distance to a crowd of peers. 

Ort, förlag, år, upplaga, sidor
Halmstad: Halmstad University Press, 2020. s. 87
Serie
Halmstad University Dissertations ; 67
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik
Identifikatorer
urn:nbn:se:hh:diva-41367 (URN)978-91-88749-43-7 (ISBN)978-91-88749-42-0 (ISBN)
Disputation
2020-01-31, J102 Wigforss, Kristian IV:s väg 3, Halmstad, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2020-01-14 Skapad: 2020-01-10 Senast uppdaterad: 2020-01-14Bibliografiskt granskad
2. Self-Monitoring using Joint Human-Machine Learning: Algorithms and Applications
Öppna denna publikation i ny flik eller fönster >>Self-Monitoring using Joint Human-Machine Learning: Algorithms and Applications
2020 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Halmstad: Halmstad University Press, 2020. s. 45
Serie
Halmstad University Dissertations ; 69
Nyckelord
self-monitoring, anomaly detection, machine learning
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
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 (Engelska)
Opponent
Handledare
Forskningsfinansiär
KK-stiftelsen, 20160103
Tillgänglig från: 2020-01-31 Skapad: 2020-01-29 Senast uppdaterad: 2020-01-31Bibliografiskt granskad

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