AI-Driven Predictive Maintenance for Autonomous Vehicles for Product-Service System Development

AI-Driven Predictive Maintenance for Autonomous Vehicles for Product-Service System Development

Abstract

The paper presents an Artificial Intelligence-driven approach to predictive maintenance for Product-Service System (PSS) development. This study focuses on time-based and condition-based maintenance, utilizing variational autoencoders to identify both predicted and unpredicted maintenance issues in autonomous haulers. By analyzing data patterns and forecasting future values, this approach enables proactive maintenance and informed decision-making in the early stages of PSS development.

The inclusion of interaction terms enhances the model’s ability to capture the interdependencies among system components, addressing hidden failure modes. Comprehensive evaluations demonstrate the effectiveness and robustness of the developed models, showcasing resilience to noise and variations in operational data.

The integration of predictive maintenance with PSS development offers a strategic advantage, providing insights into vehicle performance early in the development phases. This empowers decision-makers for efficient resource allocation and proactive maintenance planning. The research highlights the limitations and potential areas of improvement while also emphasizing the practical applicability and significance of the developed models in enhancing PSS development.

Keywords

Predictive Maintenance, Autonomous Haulers, Product-Service Systems, Artificial Intelligence, Decision-Making

Reference

Aeddula, O., Martin Frank, Ryan Ruvald, Christian Johansson Askling, Johan Wall, Tobias Larsson (2024) AI-Driven Predictive Maintenance for Autonomous Vehicles for Product-Service System Development, Procedia CIRP, Volume 128, 2024, Pages 84-89. https://doi.org/10.1016/j.procir.2024.06.008

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Categories: Publications, Research