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Production 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. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2012-07-13 16:04:57.829

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Large quantities of bulk grain are moved using graincars in Canada and other parts of the world. Automation has not progressed significantly in the grain industry probably because the market is limited for automated systems. A prototype of a robot (“Grain-o-bot”) using machine vision to automatically open and close graincar hopper gates and detect the contents of the graincar was built and studied. The “Grain-o-bot” was a Cartesian robot equipped with two cameras and an opening tool as the end-effector. One camera acted as the eye to determine the sprocket location, and guided the end-effector to the sprocket opening. For most applications, machine vision solutions based on pattern recognition were developed using images acquired in a laboratory setting. Major constraints with these solutions occurred when implementing them in real world applications. So the first step for this automation was to correctly identify the hopper gate sprocket on the grain car. Algorithms were developed to detect and identify the sprocket under proper lighting conditions with 100% accuracy. The performance of the algorithms was also evaluated for the identification of the sprocket on a grain car exposed to different lighting conditions, which are expected to occur in typical grain unloading facilities. Monochrome images of the sprocket from a model system were acquired using different light. Correlation and pattern recognition techniques using a template image combined with shape detection were used for sprocket identification. The images were pre-processed using image processing techniques, prior to template matching. The template image developed from the light source that was similar to the light source used to acquire ii images was more successful in identifying the sprocket than the template image developed using different light sources. A sample of the graincar content was taken by slightly opening and immediately closing the hopper gates. The sample was identified by taking an image using the second camera and performing feature matching. An accuracy of 99% was achieved in identifying Canada Western Red Spring (CWRS) wheat and 100% for identifying barley and canola.

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