TRANS-MAINT - Transforming Mining Maintenance: Societal and Human Perspectives for a Sustainable Future

This project aims to uncover how patterns of human–technology interaction in Swedish mining maintenance affect key system outcomes, Reliability, Availability, Maintainability, and Safety (RAMS), and to use these insights to design maintenance support tools and training that advance both operational performance and sustainable transitions. Drawing from the SMART-MINE (funded by Vinnova) and From data to safety (await decision from AFA Försäkring) projects’ foundations in augmented reality and technology acceptance, and in the analysis of accidents and risk, respectively, this PhD project will focus on the sociotechnical interaction between humans and technology that shapes maintenance performance. Grounded in Human Work Science, Maintenance Engineering, and Sociology, we emphasise generating knowledge and transferring it via training and organisational policy.

Supervisor: Phillip Tretten, Associate Professor, Hunman Work Sciences, ETKS

Subject description

The research subject of Work Science has, since 1974, conducted research and education in work science within both the technical and social science sectors. We are part of the Human and Technology division. We study how people develop, are influenced by, and interact with technology, organizations, work environments, working life, the environment, and society. Both consequences and development. Both problems and opportunities.

Co-supervisor: Mattias Holmgren, Senior Lecturer, Operation and Maintenance, SBN.

Project description

The scientific contribution centres on three interconnected research domains. First, the project will advance understanding of sociotechnical interaction by analysing how maintenance affects outcomes in mining environments (e.g., productivity, safety). This includes examining how data and reports, in combination with digital support systems, shape decision-making under conditions of uncertainty and risk. Second, the research will contribute to maintenance engineering by developing frameworks that integrate human and organisational factors into the analysis of reliability, availability, maintainability, and safety (RAMS). Third, the project will generate new knowledge about human–technology interaction in the context of digitalisation and automation, with a focus on how patterns of technology use, adaptation, and acceptance affect the effectiveness and resilience of predictive and preventive maintenance systems.

The research addresses a gap in current literature where technological advances in predictive maintenance often overlook human factors which ultimately determine system effectiveness. While mining operations increasingly adopt IoT sensors, AI-driven analytics, and predictive maintenance platforms, the cognitive and ergonomic aspects of how maintenance personnel interact with these data-rich systems remain underexplored, as do the system-level outcomes of these interactions. This project will provide evidence-based guidelines for designing maintenance systems that optimise both human performance and system reliability outcomes. In short, we aim to develop data-driven maintenance practices and tools with workers and engineers to achieve better RAMS, and safer, more accepted digital operations.