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A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications
SICS Swedish ICT, Sweden.ORCID iD: 0000-0002-9890-4918
SICS Swedish ICT, Sweden.
2015 (English)In: Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference, Hollywood, Florida, United States: AAAI , 2015, 434-439 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel, unsupervised approach to detecting anomalies at the collective level. The method probabilistically aggregates the contribution of the individual anomalies in order to detect significantly anomalous groups of cases. The approach is unsupervised in that as only input, it uses a list of cases ranked according to its individual anomaly score. Thus, any anomaly detection algorithm can be used for scoring individual anomalies, both supervised and unsupervised approaches. The applicability of the proposed approach is shown by applying it to an artificial data set and to two industrial data sets — detecting anomalously moving cranes (model-based detection) and anomalous fuel consumption (neighbour-based detection).

Place, publisher, year, edition, pages
Hollywood, Florida, United States: AAAI , 2015. 434-439 p.
Keyword [en]
Anomaly Detection, Anomaly Aggregation, Collective Anomaly, Group Anomaly
National Category
Engineering and Technology Computer and Information Science
Identifiers
URN: urn:nbn:se:mdh:diva-28160OAI: oai:DiVA.org:mdh-28160DiVA: diva2:818151
Conference
The 28th International FLAIRS Conference Flairs-28, 18 May 2015, Hollywood, Florida, United States
Projects
ITS-EASY Post Graduate School for Embedded Software and Systems
Available from: 2015-06-08 Created: 2015-06-08 Last updated: 2015-09-22Bibliographically approved
In thesis
1. A Data-Driven Approach to Remote Fault Diagnosis of Heavy-duty Machines
Open this publication in new window or tab >>A Data-Driven Approach to Remote Fault Diagnosis of Heavy-duty Machines
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Heavy-duty machines are equipment constructed for working under rough conditions and their design is meant to withstand heavy workloads. However, the last decades technical development in cheap electronically components have lead to an increase of electrical systems in traditionally mainly mechanical systems of heavy-duty machines. As the complexity of these machines increases, so does the complexity of detecting and diagnosing machine faults. However, the addition of new electrical systems, such as on-board computational power and telematics, makes it possible to add new sensors that measure signals relevant for fault detection and diagnosis, and to process signals on-board or off-board the machines.

In this thesis, we address the diagnostic problem by investigating data-driven methods for remote diagnosis of heavy-duty machines, where a part of the analysis is performed on-board the machine (fault detection), while another part is performed off-board the machine (fault classification). We propose a diagnostic framework where we use a novel combination of methods for each step in the diagnosis. On-board the machine, we have used logistic regression as an anomaly detector to detect faults that will lead to a stream of individual cases classified as anomalous or not. Then, either on-board or off-board, we can use a probabilistic anomaly detector to identify whether the stream of cases is truly anomalous when we look at the stream of cases as a group. The anomalous group of cases is called a composite case. Thereafter, off-board the machine, each anomalous individual case is classified into a fault type using a case-based reasoning approach to fault diagnosis. In the final step, we fuse the individual classifications into a single aggregated classification for the composite case. In order to be able to assess the reliability of a diagnosis, we also propose a novel case-based approach to estimating the reliability of probabilistic predictions. It can, for instance, be used for assessing the confidence of the classification of a composite case given historical data of the predictive reliability.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2015
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 189
Series
SICS Dissertation Series, ISSN 1101-1335 ; 73
National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-29097 (URN)978-91-7485-234-9 (ISBN)
Public defence
2015-11-10, Omega, Mälardalens högskola, Västerås, 14:00 (English)
Opponent
Supervisors
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2015-10-15Bibliographically approved

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http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS15/paper/view/10437

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Citation style
  • apa
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Language
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Output format
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