Marco Aurélio da Silva Cruz
Academic and research departments
Surrey Institute for People-Centred Artificial Intelligence (PAI), Centre for Vision, Speech and Signal Processing (CVSSP).About
My research project
Multimodal AI Foundation Models for Smart Predictive and Prescriptive MaintenanceThis PhD research focuses on advancing Smart Predictive and Prescriptive Maintenance through the development of intelligent, AI-driven methodologies for industrial systems. The work investigates how equipment health can be continuously assessed using multimodal data (e.g. sensor streams, maintenance records, and technical documentation, etc.), in order to anticipate failures and prescribe optimal actions that prevent or mitigate losses. By integrating state-of-the-art artificial intelligence techniques, including foundation models, computer vision, natural language processing, time-series analysis, and physics-informed neural networks, the research aims to create trustworthy, ethical, and adaptable maintenance tools applicable across multiple industry sectors. Aligned with the principles of Industry 5.0 and the UN Sustainable Development Goal 9, this research emphasizes people-centred innovation, sustainability, and human–machine collaboration to enhance efficiency, safety, resilience, and productivity in modern industrial environments.
Supervisors
This PhD research focuses on advancing Smart Predictive and Prescriptive Maintenance through the development of intelligent, AI-driven methodologies for industrial systems. The work investigates how equipment health can be continuously assessed using multimodal data (e.g. sensor streams, maintenance records, and technical documentation, etc.), in order to anticipate failures and prescribe optimal actions that prevent or mitigate losses. By integrating state-of-the-art artificial intelligence techniques, including foundation models, computer vision, natural language processing, time-series analysis, and physics-informed neural networks, the research aims to create trustworthy, ethical, and adaptable maintenance tools applicable across multiple industry sectors. Aligned with the principles of Industry 5.0 and the UN Sustainable Development Goal 9, this research emphasizes people-centred innovation, sustainability, and human–machine collaboration to enhance efficiency, safety, resilience, and productivity in modern industrial environments.