Document Type

Article

Publication Date

2025

DOI

10.1007/s44289-025-00069-2

Publication Title

Discover Oceans

Volume

2

Issue

1

Pages

33 (1-30)

Abstract

This study introduces a BiGMM-HMM Integration Framework designed to improve predictive maintenance strategies for naval vessel propulsion systems, addressing the need for efficient and reliable operation in marine engineering applications. The framework effectively manages multimodal sensor data by leveraging a unique combination of Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in a bidirectional architecture. It analyses the dynamic interactions between sensors and subsystems. Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. In contrast, Method 2 processes the entire dataset directly. Experimental results demonstrate that Method 1 outperforms Method 2, achieving an accuracy of 91%, precision of 94%, recall of 91%, and an F1-score of 91%, compared to 87%, 90%, 87%, and 88% for Method 2, respectively. These findings underscore the role of feature selection, clustering, and dimensionality reduction in predictive analytics for marine systems. Utilizing two GMMs—one for label inference and another for transition and emission probabilities—the framework captures the multimodal characteristics of propulsion system data, resulting in improved health state prediction. Applied to a naval propulsion system dataset, this approach provides actionable insights into optimizing maintenance by understanding sensor interdependencies and subsystem interactions, offering substantial advancements in marine engineering operations through more effective maintenance strategies.

Rights

© 2025 The Authors.

This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Data Availability

Article states: "The dataset used in this study is titled “Condition Monitoring of Naval Propulsion Plants”, publicly available on UCI Machine Learning Repository. https://pureportal.strath.ac.uk/en/datasets/condition-based-maintenance-of-naval-propulsion-plants-data-set-v."

ORCID

0009-0004-6773-3943 (Javadnejad), 0000-0003-0144-9099 (Sousa-Poza)

Original Publication Citation

Javadnejad, F., Park, H. J., Kovacic, S., & Sousa-Poza, A. (2025). Predictive maintenance in naval vessel propulsion systems for enhanced marine operations using a BiGMM-HMM framework with divergence-based clustering. Discover Oceans, 2(1), 1-30, Article 33. https://doi.org/10.1007/s44289-025-00069-2

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