Publicação
COMBINING DIFFERENT DATA SOURCES FOR INDUSTRIAL PROCESS MONITORING EXPERIMENTING IN A CLASSIC CAR RESTORATION WORKSHOP
| Resumo: | The historical importance, the aesthetics, the build quality, and the rarity are charac- teristic features that individually or collectively define a car as a classic. Keeping the authenticity of those masterpieces, i.e., maintaining them as close as possible to when they left the factory, requires expert restoration services. For this purpose, there are guidelines defined for classic car restoration. Monitoring the classic car restoration process so that its evidence is recorded automat- ically is essential to reduce the need for a workshop supervisor (man-in-the-middle) to do this process control manually. Therefore, this dissertation aims to detect the restoration processes performed on a classic car, in a restoration workshop, implementing energy consumption sensing, com- bining it with localization and other sensing techniques (vibration detection). A state-of-the-art rapid systematic review was done so we could make well-informed decisions according to the system’s requirements. In addition, the research made us know more about methodologies and technologies of Intrusive Load Monitoring (ILM) systems, Localization systems, and Internet of Things (IoT)/Industrial Internet of Things (IIoT) systems. After an exhaustive comprehension of the problem, analysis requirements, and devel- opment, it was possible to reach an efficient and reliable process identification system. By combining location data, energy loads identified, and vibrations detection, using, along the way, Machine Learning (ML) algorithms, the system can infer some of the restora- tion processes each classic car goes through in the workshop (Painting, Curing, Sanding, Mineral Blasting), and the start and end timestamps of those processes. Also, a web application was developed to support the control of the existing sensors in the workshop. |
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| Autores principais: | Gomes, Rodrigo Ulisses Lopes |
| Assunto: | Process Activity Recognition IoT IIoT Intrusive Load Monitoring Pattern Recognition Machine Learning Indoor Location |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The historical importance, the aesthetics, the build quality, and the rarity are charac- teristic features that individually or collectively define a car as a classic. Keeping the authenticity of those masterpieces, i.e., maintaining them as close as possible to when they left the factory, requires expert restoration services. For this purpose, there are guidelines defined for classic car restoration. Monitoring the classic car restoration process so that its evidence is recorded automat- ically is essential to reduce the need for a workshop supervisor (man-in-the-middle) to do this process control manually. Therefore, this dissertation aims to detect the restoration processes performed on a classic car, in a restoration workshop, implementing energy consumption sensing, com- bining it with localization and other sensing techniques (vibration detection). A state-of-the-art rapid systematic review was done so we could make well-informed decisions according to the system’s requirements. In addition, the research made us know more about methodologies and technologies of Intrusive Load Monitoring (ILM) systems, Localization systems, and Internet of Things (IoT)/Industrial Internet of Things (IIoT) systems. After an exhaustive comprehension of the problem, analysis requirements, and devel- opment, it was possible to reach an efficient and reliable process identification system. By combining location data, energy loads identified, and vibrations detection, using, along the way, Machine Learning (ML) algorithms, the system can infer some of the restora- tion processes each classic car goes through in the workshop (Painting, Curing, Sanding, Mineral Blasting), and the start and end timestamps of those processes. Also, a web application was developed to support the control of the existing sensors in the workshop. |
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