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Identifying Discriminating Features in Time Series Data for Diagnosis of Industrial Machines
2007 (English)In: The 24th annual workshop of the Swedish Artificial Intelligence Society, May, 2007, Boras, Sweden,, 2007Conference paper, Published paper (Refereed)
Abstract [en]

Reducing the inherent high dimensionality in time series data is a desirable goal. Algorithms used for classi¯cation can easily be misguided if presented with data of to high dimension. E.g. the k-nearest neighbor algorithm which is often used for case-based classi¯cation per- forms best on smaller dimensions with less than 20 attributes. In this paper we address the problem using a time series case base and a feature discrimination approach incorporating an unsupervised combination of a search function based on statistical feature discrimination and a crite- rion function ¯nding the global maximum of discriminating power in the range the search function. Feature vectors for case indexing is computed with respect to this information. For evaluation, previously classi¯ed cur- rent measurements from an electrical motor driving the gearbox of axis 4 on an industrial robot were used. The results were promising and we managed to correctly classify measurements from healthy and unhealthy gearboxes.

Place, publisher, year, edition, pages
2007.
Identifiers
URN: urn:nbn:se:mdh:diva-2740OAI: oai:DiVA.org:mdh-2740DiVA, id: diva2:115403
Available from: 2008-11-11 Created: 2008-11-11 Last updated: 2014-06-26Bibliographically approved
In thesis
1. Fault Diagnosis of Industrial Machines using Sensor Signals and Case-Based Reasoning
Open this publication in new window or tab >>Fault Diagnosis of Industrial Machines using Sensor Signals and Case-Based Reasoning
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Industrial machines sometimes fail to operate as intended. Such failures can be more or less severe depending on the kind of machine and the circumstances of the failure. E.g. the failure of an industrial robotcan cause a hold-up of an entire assembly line costing the affected company large amounts of money each minute on hold. Research is rapidly moving forward in the area of artificial intelligence providing methods for efficient fault diagnosis of industrial machines. The nature of fault diagnosis of industrial machines lends itself naturally to case-based reasoning. Case-based reasoning is a method in the discipline of artificial intelligence based on the idea of assembling experience from problems and their solutions as ”cases” for reuse in solving future problems. Cases are stored in a case library, available for retrieval and reuse at any time.By collecting sensor data such as acoustic emission and current measurements from a machine and representing this data as the problem part of a case and consequently representing the diagnosed fault as the solution to this problem, a complete series of the events of a machine failure and its diagnosed fault can be stored in a case for future use.

Place, publisher, year, edition, pages
Västerås: Mälardalens högskola, 2009. p. 186
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 76
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-6539 (URN)978-91-86135-32-4 (ISBN)
Public defence
2009-09-18, Pathos, Mälardalens högskola, R-2, Västerås, 13:00 (English)
Opponent
Supervisors
Available from: 2009-07-13 Created: 2009-07-06 Last updated: 2018-01-13Bibliographically approved

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  • apa
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