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Fault Diagnosis of Industrial Robots using Acoustic Signals 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 Innovation, Design and Product Development.ORCID iD: 0000-0002-0729-0122
2004 (English)In: Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science, vol 3155, 2004, p. 686-701Conference paper, Published paper (Refereed)
Abstract [en]

In industrial manufacturing rigorous testing is used to ensure that the delivered products meet their specifications. Mechanical maladjustment or faults often show their presence as deviations compared to a normal sound pro-file. This is the case in robot assembly, the selected application domain for the system. Manual diagnosis based on sound requires extensive experience, and the experience is often acquired through costly mistakes and reduced production efficiency or quality loss caused by missed faults. The acquired experience is also difficult to preserve and transfer, and often lost if personnel leave the task of testing and fault diagnosis. We propose a Case-Based Reasoning approach to collect and preserve experience. The solution enables fast experience transfer and leads to less experienced personnel required to make more reliable and informed testing. Sounds from normal and faulty equipment are recorded and stored in a case library together with a diagnosis. Addition of new validated sound profiles continuously improves the system’s performance. The system can preserve and transfer experience between technicians, reducing overall fault identification time and increases quality by reduced number of missed faults. The original sound recordings are stored in form of the extracted features to-gether with other experience, e.g. instructions, additional tests, advice, user feedback etc.

Place, publisher, year, edition, pages
2004. p. 686-701
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 3155
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-2241DOI: 10.1007/978-3-540-28631-8_50OAI: oai:DiVA.org:mdh-2241DiVA, id: diva2:114904
Conference
European Conference on Case-Based Reasoning ECCBR 2004
Available from: 2008-11-11 Created: 2008-11-11 Last updated: 2017-02-10Bibliographically 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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Funk, PeterBengtsson, Marcus

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