Marine diesel engine reliable intelligent fault diagnosis method based on the generalized multi-source information fusion.

ABSTRACT: Diesel engines provide essential power and energy guarantee for vessels. Due to scarce fault samples and complex parameter-fault coupling, traditional methods struggle in marine diesel engine diagnosis, underscoring the need for reliable intelligent approaches based on multi-source thermal parameter fusion. This article develops a reliable intelligent fault diagnosis method based on generalized multi-source information fusion. Key parameters are selected using Pearson correlation and mutual information, while an improved Bayesian optimization algorithm automatically tunes random forest parameters to enhance accuracy. TreeSHAP interprets parameter influence, guiding feature selection for retraining. An improved Dempster-Shafer evidence fusion strategy with Shannon entropy and Jousselme dist

SUBMITTER: Xie Z 

PROVIDER: S-EPMC12811478 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature