A methodology to develop and manage data-driven models for marine engine long-term health prognosis

Tools

Jeon, Jaehan and Theotokatos, Gerasimos (2025) A methodology to develop and manage data-driven models for marine engine long-term health prognosis. ISA Transactions. ISSN 0019-0578 (https://doi.org/10.1016/j.isatra.2025.09.030)

[thumbnail of Jeon-Theotokatos-ISAT-2025-A-methodology-to-develop-and-manage-data-driven-models-for-marine-engine]
Preview
 Text. Filename: Jeon-Theotokatos-ISAT-2025-A-methodology-to-develop-and-manage-data-driven-models-for-marine-engine.pdf
License: Creative Commons Attribution 4.0 logo
Download (6MB)| Preview

Abstract

This study proposes a novel methodology to develop and manage data-driven models for ship machinery Prognostics and Health Management (PHM). A four-stroke marine engine is investigated considering exhaust valve wear degradation. Simulated datasets are generated using a physics-based digital twin integrated with stochastic degradation models. Health indicators (HI) construction and forecast sub-models are developed, based on Multi-Layer Perceptron and Bayesian Neural Networks, respectively. Data-driven model management employs error and uncertainty metrics for deciding re-training of HI forecast sub-models, resulting in R2 increases from 0.24 to 0.89 and from 0.26 to 0.94 in Cases 1 and 2, respectively. This is the first study that integrates thermodynamic models with stochastic degradation models to develop marine engine digital twins, while also introducing data-driven model management, thus contributing to the PHM system adoption by the maritime industry.

ORCID iDs

Jeon, Jaehan ORCID logoORCID: https://orcid.org/0009-0001-9264-3753 and Theotokatos, Gerasimos ORCID logoORCID: https://orcid.org/0000-0003-3547-8867;
CORE (COnnecting REpositories)