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dc.contributor.authorSzkilnyk, Gregory
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2012-07-13 16:04:57.829en
dc.date.accessioned2012-07-17T18:17:36Z
dc.date.available2012-07-17T18:17:36Z
dc.date.issued2012-07-17
dc.identifier.urihttp://hdl.handle.net/1974/7322
dc.descriptionThesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2012-07-13 16:04:57.829en
dc.description.abstractProduction downtime caused by machine faults presents a major area of concern for the manufacturing industry and can especially impact the productivity of assembly systems. Traditional fault detection systems use a variety of conventional sensors that measure operating variables such as pressure, force, speed, current and temperature. Faults are detected when a reading from one of these sensors exceeds a preset threshold or does not match the predicted value provided by a mathematical model of the system. The primary disadvantage of these methods is that the relationship between sensor reading and fault is often indirect (if one exists at all). This can lead to time delays between fault occurrence and ‘fault reading’ from a sensor, during which additional machine damage could accumulate. This thesis describes progress with a project whose goal is to examine the effectiveness and feasibility of using machine vision to detect ‘visually cued’ machine faults in automated assembly equipment. It is proposed that machine vision technology could complement traditional methods and improve existing detection systems. Two different vision-based fault detection methods were developed and tests were conducted using a laboratory-scale assembly machine that assembles a simple 3-part component Typical faults that occurred with this machine were targeted for inspection. The first method was developed using Automated Visual Inspection (AVI) techniques that have been used extensively for quality inspection of manufactured products. The LabVIEW 2010 software was used to develop the system. Test results showed that the Colour Inspection tool performed the best with 0% false negative and false positive fault detection rates. Despite some success, this approach was found to be limited as it was unable to detect faults that varied in physical appearance or those that had not been identified prior to testing. The second method was developed using a video event detection method (spatiotemporal volumes) that has previously been used for traffic and pedestrian monitoring. This system was developed with MATLAB software and demonstrated strong false negative and false positive fault detection rates. It also showed the ability to detect faults that had not previously been identified as well as those that varied in appearance. Recommendations were made for future work to further explore these methods.en_US
dc.languageenen
dc.language.isoenen_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectmachine visionen_US
dc.subjectautomationen_US
dc.subjectassemblyen_US
dc.subjectfault detectionen_US
dc.titleVision-based Fault Detection in Assembly Automationen_US
dc.typethesisen_US
dc.description.degreeMasteren
dc.contributor.supervisorSurgenor, Brian W.en
dc.contributor.departmentMechanical and Materials Engineeringen


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