Abstract
The paper discusses the independent cart technology, which utilizes linear motors to move carts along a predetermined track autonomously. This technology offers control of individual speed profiles for each section along the track, frictionless propulsion mechanism, and the ability to start and stop loads quickly. Nevertheless, the initial cost of these systems is substantial, and regular condition monitoring is required to ensure optimal performance and long-term economic benefits. The paper provides an overview of various condition monitoring and signal processing techniques for analysis, including data-driven modeling with machine learning algorithms. The article presents an experimental setup based on the independent cart system and outlines a strategy for data acquisition that emphasizes specific conditions during each run of the system. The collected data is critical in monitoring the independent cart system’s condition and developing expertise in identifying different types of faults and their precise locations, utilizing hybrid modeling approaches.
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Acknowledgements
Authors gratefully acknowledge the European Commission for its support of the Marie Sklodowska Curie Program through the H2020 ETN MOIRA project (GA 955681). Authors would like to thank Mr. Giovanni Paladini for his support in troubleshooting the issues related to the experimental setup.
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Jabbar, A., D’Elia, G., Cocconcelli, M. (2024). Experimental Setup for Non-stationary Condition Monitoring of Independent Cart Systems. In: Kumar, U., Karim, R., Galar, D., Kour, R. (eds) International Congress and Workshop on Industrial AI and eMaintenance 2023. IAI 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-39619-9_38
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DOI: https://doi.org/10.1007/978-3-031-39619-9_38
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