The Experts below are selected from a list of 2610 Experts worldwide ranked by ideXlab platform
E.r. Davies - One of the best experts on this subject based on the ideXlab platform.
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High-speed processor for realtime Visual Inspection
Microprocessors and Microsystems, 1991Co-Authors: J. M. Edmonds, E.r. DaviesAbstract:Abstract The paper describes a Complete Visual Inspection system, including the SIP sequential image processor, accompanying software and an algorithm for inspecting biscuits in production. It also analyses the performance of the SIP system. Bit-slice technology is used because of the constraints imposed by industrial manufacture, particularly cost, speed and adaptability. The SIP has been found capable of coping with product rates of ∼ 11 products per second in a typical (rectangular biscuit) Inspection task, though with suitable additional circuitry this capability should rise to ∼ 30 products per second.
J. M. Edmonds - One of the best experts on this subject based on the ideXlab platform.
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High-speed processor for realtime Visual Inspection
Microprocessors and Microsystems, 1991Co-Authors: J. M. Edmonds, E.r. DaviesAbstract:Abstract The paper describes a Complete Visual Inspection system, including the SIP sequential image processor, accompanying software and an algorithm for inspecting biscuits in production. It also analyses the performance of the SIP system. Bit-slice technology is used because of the constraints imposed by industrial manufacture, particularly cost, speed and adaptability. The SIP has been found capable of coping with product rates of ∼ 11 products per second in a typical (rectangular biscuit) Inspection task, though with suitable additional circuitry this capability should rise to ∼ 30 products per second.
Kalb, Benedikt Michael - One of the best experts on this subject based on the ideXlab platform.
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Digital transformaion in the oil and gas industry : how do unmanned aerial vehicles, specifically drones, contribute to improving the offshore Inspection process of oil and gas platforms?
2019Co-Authors: Kalb, Benedikt MichaelAbstract:Regular Inspections of oil and gas (O&G) platforms are vital for production, maintenance, safety, and the environment. Due to their location, offshore O&G facilities are exposed to hazardous and extreme conditions. In order to advance digitalization in the O&G sector, high-tech robots must be integrated. Traditional Inspection methods, like Rope Access, Scaffolding, and Manned Helicopters imply significant challenges such as building scaffolding, sending Inspection teams into hazardous environments, production downtime and high financial costs. Past research has identified how the introduction of unmanned aerial vehicles (UAVs) can meet the challenges by reducing costs, increasing safety conditions, increasing efficiency and improving accuracy. This dissertation presents the current state of research in the offshore O&G industry, identifies and quantifies cost and time factors. It compares the traditional Inspection methods to the UAV Inspection method by applying qualitative and quantitative methods of primary data gathering from industry expert interviews and secondary company data. This research shows that switching to UAV Inspection allows a worldwide saving potential of USD 12B and market potential of USD 13M per Inspection cycle. The Complete Visual Inspection, including Flare, Underdeck, and Drilling Derrick is on average seven times faster than traditional methods. Introducing UAV Inspection improves safety, as drone pilots operate up to 500m away from high-risk areas on the platform. Combining high-tech drones and AI-based software allows to generate a huge amount of data and build Complete 3D models which can be reproduced continuously to detect trends in the state of the platform and apply Predictive Maintenance.Inspeções regulares das plataformas de petróleo e gás (O&G) são vitais para a produção, manutenção, segurança e ambiente. Devido à sua localização, as instalações de O&G offshore estão expostas a condições extremas. Métodos tradicionais de inspeção (acesso por corda, andaimes, helicópteros tripulados) implicam desafios significativos: construção de andaimes, envio de equipas para ambientes perigosos, tempo de inatividade de produção, e altos custos financeiros. Pesquisas anteriores mostraram como a introdução de veículos aéreos não tripulados (UAVs) pode colmatar desafios: redução de custos, aumento de condições de segurança, de eficiência e de precisão. Esta dissertação apresenta o estado atual da pesquisa na indústria de O&G offshore, identifica e quantifica os fatores de custo e tempo, compara os métodos de inspeção tradicionais com o método de inspeção UAV, aplicando métodos qualitativos e quantitativos de coleção de dados primários de entrevistas com especialistas da indústria e dados secundários da empresa. Esta pesquisa mostra que mudar para inspeção UAV permite um potencial de economia mundial de USD 12B e potencial de mercado de USD 13M por ciclo de inspeção. A inspeção Visual completa é, em média, sete vezes mais rápida do que os métodos tradicionais. A introdução da inspeção UAV melhora a segurança: os pilotos de drones operam até 500m de distância, na plataforma. A combinação de drones de alta tecnologia e software baseado em IA permite gerar uma elevada quantidade de dados e construir modelos 3D que podem ser reproduzidos continuamente para detetar tendências no estado da plataforma e aplicar a Manutenção Preditiva
J Eshelby - One of the best experts on this subject based on the ideXlab platform.
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AUTOMATED COLLECTION AND DETECTION OF RAIL SURFACE DEFECTS
1991Co-Authors: Sue Mcneil, Behnam Motazed, Roemer Alfelor, T Short, J EshelbyAbstract:Accurate and Complete Visual Inspection of the surface of the rail head is time consuming and costly. However, records of the size, location, and type of surface flaws can be used to develop grinding strategies and complement rail profile and fatigue defect data for assessing the overall condition of the rails. This paper describes a prototype system for collecting surface defect data at high speeds and automatically processing the data to identify defective sections.
Sue Mcneil - One of the best experts on this subject based on the ideXlab platform.
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AUTOMATED COLLECTION AND DETECTION OF RAIL SURFACE DEFECTS
1991Co-Authors: Sue Mcneil, Behnam Motazed, Roemer Alfelor, T Short, J EshelbyAbstract:Accurate and Complete Visual Inspection of the surface of the rail head is time consuming and costly. However, records of the size, location, and type of surface flaws can be used to develop grinding strategies and complement rail profile and fatigue defect data for assessing the overall condition of the rails. This paper describes a prototype system for collecting surface defect data at high speeds and automatically processing the data to identify defective sections.