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Fault Diagnosis in Industry Using Sensor Readings and Case-Based Reasoning
Mälardalen University, Department of Computer Science and Electronics.
Mälardalen University, Department of Computer Science and Electronics.ORCID iD: 0000-0002-5562-1424
Mälardalen University, Department of Computer Science and Electronics.ORCID iD: 0000-0001-9857-4317
2004 (English)In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, Vol. 15, no 1, p. 41-46Article in journal (Refereed) Published
Abstract [en]

Fault diagnosis of industrial equipments becomes increasingly important for improving the quality of manufacturing and reducing the cost for product testing. Developing a fast and reliable diagnosis system presents a challenge issue in many complex industrial scenarios. The major difficulties therein arise from contaminated sensor readings caused by heavy background noise as well as the unavailability of experienced technicians for support. In this paper we propose a novel method for diagnosis of faults by means of case-based reasoning and signal processing. The received sensor signals are processed by wavelet analysis to filter out noise and at the same time to extract a group of related features that constitutes a reduced representation of the original signal. The derived feature vector is then forwarded to a classification component that uses case-based reasoning to recommend a fault class for the probe case. This recommendation is based on previously classified cases in a case library. Case-based diagnosis has attractive properties in that it enables reuse of past experiences whereas imposes no demand on the size of the case base. The proposed approach has been applied to fault diagnosis of industrial robots at ABB Robotics and the results of experiments are very promising.

Place, publisher, year, edition, pages
2004. Vol. 15, no 1, p. 41-46
National Category
Software Engineering Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:mdh:diva-2232ISI: 000226874700006Scopus ID: 2-s2.0-10044228485OAI: oai:DiVA.org:mdh-2232DiVA, id: diva2:114895
Available from: 2008-11-11 Created: 2008-11-11 Last updated: 2018-01-13Bibliographically 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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