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Intelligent management of physical assets to maximise operational efficiency and reduce environmental impact
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Esta tese explora o campo da Gestão Inteligente de Ativos (IAM) com ênfase na integração de estratégias avançadas e tecnologias inovadoras para otimizar a gestão de ativos físicos em contextos industriais. Em resposta às crescentes exigências de eficiência operacional e responsabilidade ambiental, procede-se a uma análise crítica das práticas atuais de gestão de ativos, com o objetivo de identificar oportunidades de melhoria de desempenho e reforço da resiliência a longo prazo.
Como contribuição principal, foi desenvolvida uma nova estrutura para Gestão Inteligente de Ativos. Concebida para integrar tecnologia, pessoas, processos e, regulamentos e políticas externos, alinhada com os princípios da Indústria 5.0, fornecendo informações valiosas para melhorar a gestão de ativos industriais e promover práticas operacionais sustentáveis.
Para validar e aplicar a estrutura, foi realizado um caso de estudo em colaboração com a empresa LKAB, com foco na análise de dois moinhos industriais. A avaliação incidiu sobre a eficácia das práticas de manutenção e o desempenho operacional, através da análise detalhada da frequência de paragens, da duração dos tempos de inatividade e dos modos de falha. As ferramentas analíticas utilizadas incluem indicadores-chave de desempenho (Key Performance Indicators - KPIs) de manutenção, Análise de Modos de Falha, Efeitos e Criticidade (Failure Modes Effects, and Criticality Analysis – FMECA) e diagramas de Causa e Efeito. Além disso, foram simulados os potenciais benefícios decorrentes da redução dos tempos de inatividade, com projeções de ganhos na produção e poupança energética.
Para pesquisas futuras, recomenda-se a aplicação de ferramentas analíticas adicionais, como distribuições de Weibull ou Pareto, bem como KPIs relacionados à energia e custos. Além disso, estudos futuros devem analisar a interface entre humanos e sistemas IAM, e avaliar como a estrutura proposta pode ser adaptada em vários setores que enfrentam desafios operacionais.
This thesis explores the field of Intelligent Asset Management (IAM), emphasizing the integration of advanced strategies and technologies to enhance the management of physical assets in industrial contexts. In response to the demand for operational efficiency and environmental responsibility, this thesis critically examines existing asset management practices to identify opportunities for performance improvement and enhance long-term resilience. As a key contribution, new framework for Intelligent Asset Management was developed. Designed to integrate technology, people, process and external regulations and policies, align with the Industry 5.0 principles, providing valuable insights to improve industrial asset management and promote sustainable operational practices. To validate and apply the framework, a case study was conducted in collaboration with LKAB company, focusing on the analysis of two grinding mills. The study assessed maintenance effectiveness and operational performance through a detailed examination of shutdown frequency, downtime duration, and failure modes. Analytical tools employed include maintenance Key Performance Indicators (KPIs), Failure Modes, Effects, and Criticality Analysis (FMECA), and Cause-and-Effect diagrams. Additionally, the potential benefits from downtime reduction were modelled, projecting corresponding gains in production efficiency and energy savings. For future research, the application of additional analytical tools such as Weibull and Pareto distributions, as well as energy and cost-related KPIs, is recommended. Furthermore, future studies should analyse the interface between humans and IAM systems and evaluate how the proposed framework can be adapted across various industries facing operational challenges.
This thesis explores the field of Intelligent Asset Management (IAM), emphasizing the integration of advanced strategies and technologies to enhance the management of physical assets in industrial contexts. In response to the demand for operational efficiency and environmental responsibility, this thesis critically examines existing asset management practices to identify opportunities for performance improvement and enhance long-term resilience. As a key contribution, new framework for Intelligent Asset Management was developed. Designed to integrate technology, people, process and external regulations and policies, align with the Industry 5.0 principles, providing valuable insights to improve industrial asset management and promote sustainable operational practices. To validate and apply the framework, a case study was conducted in collaboration with LKAB company, focusing on the analysis of two grinding mills. The study assessed maintenance effectiveness and operational performance through a detailed examination of shutdown frequency, downtime duration, and failure modes. Analytical tools employed include maintenance Key Performance Indicators (KPIs), Failure Modes, Effects, and Criticality Analysis (FMECA), and Cause-and-Effect diagrams. Additionally, the potential benefits from downtime reduction were modelled, projecting corresponding gains in production efficiency and energy savings. For future research, the application of additional analytical tools such as Weibull and Pareto distributions, as well as energy and cost-related KPIs, is recommended. Furthermore, future studies should analyse the interface between humans and IAM systems and evaluate how the proposed framework can be adapted across various industries facing operational challenges.
Descrição
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Ativos físicos Gestão inteligente de ativos Eficiência operacional Sustentabilidade Indústria 5.0
Contexto Educativo
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Licença CC
Sem licença CC