Maione, F.; Lino, P.; Maione, G.; Giannino, G. Machine Learning Algorithms for Condition-Based Maintenance of Marine Diesel Engines. Preprints2024, 2024070941. https://doi.org/10.20944/preprints202407.0941.v1
APA Style
Maione, F., Lino, P., Maione, G., & Giannino, G. (2024). Machine Learning Algorithms for Condition-Based Maintenance of Marine Diesel Engines. Preprints. https://doi.org/10.20944/preprints202407.0941.v1
Chicago/Turabian Style
Maione, F., Guido Maione and Giuseppe Giannino. 2024 "Machine Learning Algorithms for Condition-Based Maintenance of Marine Diesel Engines" Preprints. https://doi.org/10.20944/preprints202407.0941.v1
Abstract
The development of Artificial Intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are deeply changing both from a theoretical and a practical perspective, even in the field of ship propulsion systems. Namely, conventional preventive maintenance schedules audits and checks at fixed times by using statistics on component failures, but it can be improved by a predictive maintenance based on the real component health status, which is inspected by proper sensors. In this way, time and costs are saved. More in details, data-driven models, through Machine Learning (ML) algorithms, generate the expected values of monitored variables for comparison with real measurements coming from the asset, hence for a diagnosis based on the difference between expectations and observations. Then, a ML-based fault detection starts with the choice of the ML algorithm. This process is often not the consequence of an in-depth deep analysis of the different algorithms available in the literature. For that reason, the authors propose a simple procedure to support a first implementation stage of the so-called Condition Based Maintenance (CBM): a quick benchmarking of the most suitable ML algorithms useful for fault detection. The algorithms are compared by taking into account not only the algorithm output error, shown by the Mean Squared Error between predictions and data, but also the response time, which is a crucial variable for maintenance purpose.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
