Real-time inspection based on machine vision technologies is being widely used in quality control and cost reduction in a variety of application domains. The high demands on the inspection performance and low cost requirements make the algorithm design a challenging task that requires new and innovative methodologies in image processing and fusion. In this research, an integrated approach that combines novel image processing and fusion techniques is proposed for the efficient design of accurate and real-time machine vision-based inspection algorithms with an application to the food processing problem. Firstly, a general methodology is introduced for effective detection of defects and foreign objects that possess certain spectral and shape features. The factors that affect performance metrics are analyzed, and a recursive segmentation and classification scheme is proposed in order to improve the segmentation accuracy. The developed methodology is applied to real-time fan bone detection in deboned poultry meat with a detection rate of 93% and a false alarm rate of 7% from a lab-scale testing on 280 samples. Secondly, a novel snake-based algorithm is developed for the segmentation of vector-valued images. The snakes are driven by the weighted sum of the optimal forces derived from corresponding energy functionals in each image, where the weights are determined based on a novel metric that measures both local contrasts and noise powers in individual sensor images. This algorithm is effective in improving the segmentation accuracy when imagery from multiple sensors is available to the inspection system. The effectiveness of the developed algorithm is verified using (i) synthesized images (ii) real medical and aerial images and (iii) color and x-ray chicken breast images. The results further confirmed that the algorithm yields higher segmentation accuracy than monosensory methods and is able to accommodate a certain amount of registration error. This feature-level image fusion technique can be combined with pixel- and decision- level techniques to improve the overall inspection system performance.

In this study, the effect of inspection error on the product mix and quality projects selection in a manufacturing environment where rework and inspection errors exist is examined. It is assumed that the products (items) for which rework is necessary are reprocessed at a separate work center and 100% inspection is performed for the products both after rework and processing operations. Markov chain approach is used to compute yield and rework rates. In addition, nominal-the-best type of a quality loss function is used in computing quality loss due to products shipped to the customers. A linear programming (LP) model is developed to support the product mix and quality improvement project selection decisions. The use of LP model is demonstrated on an example problem. The results obtained under different experimental conditions are compared with solutions of a naive QI project selection method, improving the least capable process. The analysis shows that developed LP model is relatively better than process capability approach. Besides, according to the results obtained under different experimental conditions, the factors that have significant effect on throughput and QI project selection are being determined.

Product visual inspection is still performed manually or semi automatically in most industries from simple ceramic tile grading to complicated automotive body panel paint defect and surface quality inspection. Moreover, specular surfaces present additional challenges to conventional vision systems due to specular reflections, which may mask the true location of objects and lead to incorrect measurements. Some sophisticated optical inspection methods have already been developed for high precision surface defect inspection in recent years. Unfortunately, most of them are highly computational. Systems built on those methods are either inapplicable or costly to achieve real-time inspection. This thesis describes an integrated low-cost intelligent system developed to automatically capture and extract regular defects of the ceramic tiles with uniformly colored specular coatings. The proposed system is implemented on a group of smart cameras using its on-board processing ability to achieve real-time inspection. The results of this study will be used to facilitate the design of a robust, low-cost, closed-loop inspection system for a class of products with smooth specular coatings. The experimental results on real test panels demonstrate the effectiveness and robustness of proposed system.

For decades, reliability engineering techniques have been successfully applied in many industries to improve the performance of equipment maintenance management. Numerous inspection and optimization models are developed and widely used to achieve maintenance excellence, i.e. the balance of performance, risk, resources and cost to reach to an optimal solution. However, the application of all these techniques and models to medical devices is new. Hospitals, due to possessing a large number of difference devices, can benefit significantly if the optimization techniques are used properly in the equipment management processes. Most research in the area of reliability engineering for medical equipment mainly considers the devices in their design or manufacturing stage and suggests some techniques to improve the reliability. To this point, best maintenance strategies for medical equipment in their operating context have not been considered. We aim to address this gap and propose methods to improve current maintenance strategies in the healthcare industry. More specifically, we first identify or propose the criteria which are important to assess the criticality of medical devices, and propose a model for the prioritization of medical equipment for maintenance decisions. The model is a novel application of multi-criteria decision making methodology to prioritize medical devices in a hospital according to their criticality. The devices with high level of criticality should be included in the hospital’s maintenance management program. Then, we propose a method to statistically analyze maintenance data for complex medical devices with censoring and missing information. We present a classification of failure types and establish policies for analyzing data at different levels of the device. Moreover, a new method for trend analysis of censored failure data is proposed. A novel feature of this work is that it considers dependent failure histories which are censored by inspection intervals. Trend analysis of this type of data has not been discussed in the literature. Finally, we introduce some assumptions based on the results of the analysis, and develop several new models to find the optimal inspection interval for a system subject to hard and soft failures. Hard failures are instantaneously revealed and fixed. Soft failures are only rectified at inspections. They do not halt the system, although they reduce its performance or productivity. The models are constructed for two main cases with the assumption of periodic inspections, and periodic and opportunistic inspections, respectively. All numerical examples and case studies presented in the dissertation are adapted from the maintenance data received from a Canadian hospital.

For decades, reliability engineering techniques have been successfully applied in many industries to improve the performance of equipment maintenance management. Numerous inspection and optimization models are developed and widely used to achieve maintenance excellence, i.e. the balance of performance, risk, resources and cost to reach to an optimal solution. However, the application of all these techniques and models to medical devices is new. Hospitals, due to possessing a large number of difference devices, can benefit significantly if the optimization techniques are used properly in the equipment management processes. Most research in the area of reliability engineering for medical equipment mainly considers the devices in their design or manufacturing stage and suggests some techniques to improve the reliability. To this point, best maintenance strategies for medical equipment in their operating context have not been considered. We aim to address this gap and propose methods to improve current maintenance strategies in the healthcare industry. More specifically, we first identify or propose the criteria which are important to assess the criticality of medical devices, and propose a model for the prioritization of medical equipment for maintenance decisions. The model is a novel application of multi-criteria decision making methodology to prioritize medical devices in a hospital according to their criticality. The devices with high level of criticality should be included in the hospital’s maintenance management program. Then, we propose a method to statistically analyze maintenance data for complex medical devices with censoring and missing information. We present a classification of failure types and establish policies for analyzing data at different levels of the device. Moreover, a new method for trend analysis of censored failure data is proposed. A novel feature of this work is that it considers dependent failure histories which are censored by inspection intervals. Trend analysis of this type of data has not been discussed in the literature. Finally, we introduce some assumptions based on the results of the analysis, and develop several new models to find the optimal inspection interval for a system subject to hard and soft failures. Hard failures are instantaneously revealed and fixed. Soft failures are only rectified at inspections. They do not halt the system, although they reduce its performance or productivity. The models are constructed for two main cases with the assumption of periodic inspections, and periodic and opportunistic inspections, respectively. All numerical examples and case studies presented in the dissertation are adapted from the maintenance data received from a Canadian hospital.

The objectives of this thesis are to develop and validate laser ultrasonic inspection methods for on-line testing of microelectronic devices. Electronic packaging technologies such as flip chips and BGAs utilize solder bumps as electrical and mechanical connections. Since they are located hidden from view between the device and the substrate, defects such as cracks, voids, misalignments, and missing bumps are difficult to detect using non-destructive methods. Laser ultrasonic inspection is capable of detecting such defects by utilizing a high power laser pulse to induce vibrations in a microelectronic device while measuring the out of plane displacement using an interferometer. Quality can then be assessed by comparing the vibration response of a known-good device to the response of the sample under inspection. The main limitation with the implementation of laser ultrasonic inspection in manufacturing applications is the requirement to establish a known-good reference device utilizing other non-destructive methods. My work will focus on developing a method to inspect flip chip devices without requiring a previously established reference. The method will automatically examine measurement data from a large sample set to identify those devices which are most similar. The selected devices can then be utilized to compose a hybrid reference signal which can be used for comparison and defect detection. Current trends in the electronic packaging industry continue to drive toward increased solder bump density, making it increasingly difficult to generate strong ultrasonic signals in these stiffer devices. To overcome this difficulty, I propose a new excitation method which places the source of ultrasound at the inspection location for each test point on the device surface. This ensures that the same power is available for each inspection location while also increasing the signal to noise ratio. The hardware implementation of this method reduces the system complexity and required automation, which can significantly reduce equipment cost and inspection time. The implementation of the proposed excitation method in conjunction with the use of a hybrid reference signal for defect detection will improve the utility of the laser ultrasonic inspection technique to on-line inspection applications where no other non-destructive methods are currently available.

abstract: Gathering and managing software requirements, known as Requirement Engineering (RE), is a significant and basic step during the Software Development Life Cycle (SDLC). Any error or defect during the RE step will propagate to further steps of SDLC and resolving it will be more costly than any defect in other steps. In order to produce better quality software, the requirements have to be free of any defects. Verification and Validation (V&V;) of requirements are performed to improve their quality, by performing the V&V; process on the Software Requirement Specification (SRS) document. V&V; of the software requirements focused to a specific domain helps in improving quality. A large database of software requirements from software projects of different domains is created. Software requirements from commercial applications are focus of this project; other domains embedded, mobile, E-commerce, etc. can be the focus of future efforts. The V&V; is done to inspect the requirements and improve the quality. Inspections are done to detect defects in the requirements and three approaches for inspection of software requirements are discussed; ad-hoc techniques, checklists, and scenario-based techniques. A more systematic domain-specific technique is presented for performing V&V; of requirements. / Dissertation/Thesis / M.C.St. Computing Studies 2012

abstract: Every year, more than 11 million maritime containers and 11 million commercial trucks arrive to the United States, carrying all types of imported goods. As it would be costly to inspect every container, only a fraction of them are inspected before being allowed to proceed into the United States. This dissertation proposes a decision support system that aims to allocate the scarce inspection resources at a land POE (L-POE), to minimize the different costs associated with the inspection process, including those associated with delaying the entry of legitimate imports. Given the ubiquity of sensors in all aspects of the supply chain, it is necessary to have automated decision systems that incorporate the information provided by these sensors and other possible channels into the inspection planning process. The inspection planning system proposed in this dissertation decomposes the inspection effort allocation process into two phases: Primary and detailed inspection planning. The former helps decide what to inspect, and the latter how to conduct the inspections. A multi-objective optimization (MOO) model is developed for primary inspection planning. This model tries to balance the costs of conducting inspections, direct and expected, and the waiting time of the trucks. The resulting model is exploited in two different ways: One is to construct a complete or a partial efficient frontier for the MOO model with diversity of Pareto-optimal solutions maximized; the other is to evaluate a given inspection plan and provide possible suggestions for improvement. The methodologies are described in detail and case studies provided. The case studies show that this MOO based primary planning model can effectively pick out the non-conforming trucks to inspect, while balancing the costs and waiting time. / Dissertation/Thesis / code developed in this dissertation / Ph.D. Industrial Engineering 2012

Att underhålla infrastruktur med hög standard är viktigt för alla länder, och att misslyckas med detta innebär allvarliga logistiska och ekonomiska konsekvenser. Kraftledningsinspektion är en betydelsefull del i detta. Denna uppsats har sökt svar på vad för- och nackdelarna är med att använda satellitövervakning av kraftledningar, samt svar på ifall teknik för satellitövervakning av kraftledningar också kan tillämpas på volymberäkningar av skog. Metoden har utgått från intervjuer med experter och relevanta företag samt litteratur som underlag. Att undersöka vilka för- och nackdelar som finns med satellitövervakning av kraftledningar var viktigt eftersom satellitövervakning är ett snabbt växande fält, men inte särskilt väl undersökt. Att undersöka huruvida teknik för kraftledningsövervakning med satelliter är tillräcklig för att estimera skogsvolym bedömdes vara värdefullt eftersom skogsvolym idag estimeras med luftburen LiDAR, medan luftburen LiDAR påstods vara signifikant mycket dyrare överlag än satellitmätningar. Det fanns alltså en eventuell ekonomisk fördel med att estimera skogsvolym med satelliter istället för dagens luftburna mätningar. Det förväntade resultatet var att tekniken för satellitövervakning av kraftledningar är tillräcklig för att estimera skogsvolym. De största nackdelarna med satellitövervakning av kraftledningar berör problemen med att nå tillräckligt hög noggrannhet i processerna för trädidentifiering, samt att utveckla effektiva tillvägagångssätt för att utvärdera detta då underlaget gällande föreslagna och utvärderade metoder är glest. En annan nackdel visade sig vara att satellitmetoderna är svåra att göra konkurrenskraftiga i jämförelse med de etablerade luftburna LiDAR-metoderna i fråga om kostnader. Anledningen är att de högupplösta satellitbilder som ofta krävts för att nå hög noggrannhet fortfarande är dyra, även om en fördel som också identifierades var att ny och billigare satellitteknik just nu utvecklas i hög takt. Gällande denna fråga visade sig den största fördelen vara den snabba utvecklingen av nya satelliter med högre upplösning, som öppnar upp möjligheten för att komma ikapp de konventionella metoderna. Det förväntade resultatet kring huruvida satellitövervakning har ekonomiska fördelar jämfört med luftburen övervakning motsägs alltså av resultatet i denna rapport, med avseende på kraftledningsövervakning. Däremot indikerar resultatet att tekniken för satellitövervakning av kraftledningar är tillräcklig för att estimera skogsvolym, vilket överensstämmer med det förväntade resultatet. / Maintaining infrastructure of high standard is important for all countries. Failing this means severe logistical and economic consequences. Power line inspection is an important part of this. This thesis has searched for an answer to what the advantages and disadvantages are of inspecting power lines by using satellites, as well as an answer to if the technology of satellite surveillance of power lines is sufficient to estimate forest volume. The methodology of the thesis has been to turn to companies and experts in the field and to use relevant literature. Examining what the advantages and disadvantages of satellite inspection of power lines are was important since satellite surveillance is a growing field, but not very well researched. To analyze whether technology of satellite surveillance of power lines is enough to estimate forest volume was thought to be valuable since forest volume today is estimated by airborne LiDAR, while airborne LiDAR was claimed to be significantly more expensive in general than 3 satellite measurements. Thus, there was a potential economic advantage to estimate forest volume with satellites instead of airborne measurements. The expected result was that the technology of satellite surveillance of power lines is sufficient to estimate forest volume. The biggest disadvantages of satellite surveillance of power lines involve the problems of achieving high enough accuracy in the processes of tree identification, as well as developing effective formulas to evaluate this when the research material of proposed methods is sparse. Another disadvantage turned out to be that the satellite methods are hard to compete with, in comparison to the established airborne LiDAR methods and in regard to cost. The reason is that the high-resolution satellite images that often are demanded still are expensive, even though an advantage that also was identified in this thesis is that new and cheaper satellite technology is being developed at a quick rate. The biggest advantage of satellite surveillance of power lines turned out to be the quick development of new satellites with higher resolution, which enables the possibility to catch up with the conventional methods. The expected result in regard to whether satellite surveillance has economic advantages compared to airborne surveillance is contradicted in the result of this thesis, in regard to power line inspection. However, the result indicates that the technology of satellite surveillance of power lines is sufficient to estimate forest volume, which concurs with the expected result.

Ships and vessel conditions demand regular assessment to maintain their safety. In the traditional environment, their assessment was conducted using surveys and complex and time-consuming operations. But now, with the emergence of Industry 4.0 where intelligence and smart devices serve the imagery, drone-based, and many other alternative methods for inspection, the subject is obtaining considerable interest. The concept is highly effective with low cost and less disruption delivering a safer inspection approach. This study has examined Industry 4.0 technology as a quality inspection technique of a ship/vessel, examined drone-based ship inspection techniques for quality inspection of the ship/vessel without human involvement, to analyse robotic underwater surveillance methods for quality inspection of the ship/vessel, and to identify vision-based corrosion detection techniques for quality inspection of the ship/vessel. In the finding, it was revealed ship inspection through Industry 4.0 technology and other techniques can help the marine industries rely more on automated systems to gather the information that is required to be capable of authenticating process and product conformance also they can reduce human error, risks and uncover useful insights from the gathered vessel/ship data.