Open Access
ARTICLE
A Bayesian Tree-Structured Kernel Learning Approach for Gaussian Process Prognostics of Ship Propulsion Systems
Xinyu Jia1,2, Qingjie Wang1, Rongchuan Zhang3, Heng Ouyang1,*
1 State Key Laboratory of Smart Power Distribution Equipment and System, School of Mechanical Engineering, Hebei University of Technology, Tianjin, China
2 Engineering Risk Analysis Group, Technische Universität München, Arcisstr. 21, München, Germany
3 Wuhan Second Ship Design and Research Institute, Wuhan, China
* Corresponding Author: Heng Ouyang. Email: 
(This article belongs to the Special Issue: Infrastructure Resilience Enhancement Empowered by Intelligent Perception and Advanced Algorithms)
Structural Durability & Health Monitoring https://doi.org/10.32604/sdhm.2026.084325
Received 20 April 2026; Accepted 08 July 2026; Published online 04 August 2026
Abstract
Accurate state assessment and remaining useful life (RUL) prediction for ship propulsion systems are critical in ensuring maritime safety, reducing maintenance costs, and enhancing transportation network resilience. Although Gaussian process methods are widely used for RUL prediction, existing models often rely on deterministic assumptions or fixed kernel functions, which struggle to fully capture the nonlinear, multi-scale, and stochastic characteristics of degradation in ship propulsion components. This paper proposes an automatic kernel selection framework based on Bayesian tree structures, integrated with Gaussian Process Regression (GPR), for probabilistic degradation modeling and RUL prediction of critical components. The method uses additive and multiplicative operators to combine interpretable base kernels, thereby constructing a composite kernel space. A Bayesian inference strategy is employed to search for kernel structures and estimate hyperparameters simultaneously. To efficiently explore the kernel space, a two-stage beam search algorithm using model evidence is designed, along with a validation-assisted optimization mechanism to improve prediction accuracy and uncertainty calibration. Experiments were conducted on the PRONOSTIA bearing dataset and a high-fidelity ship propulsion system simulation dataset. The results show that the proposed method achieves competitive prediction accuracy and provides more reliable uncertainty quantification than fixed-kernel GPR models, supporting its application in health monitoring and risk-aware predictive maintenance of ship propulsion systems.
Keywords
Gaussian process regression; automatic kernel learning; Bayesian inference; remaining useful life; ship propulsion systems
Login
Register
Submit a Paper
Propose a Special lssue
Download PDF
Downloads
Citation Tools
