Intelligent wear mode identification system for marine diesel engines based on multi-level belief rule base methodology

Wear faults are among the chief causes of main-engine damage, significantly influencing the secure and economical operation of ships. It is difficult for engineers to utilize multi-source information to identify wear modes, so an intelligent wear mode identification model needs to be developed to assist engineers in diagnosing wear faults in diesel engines. For this purpose, a multi-level belief rule base (BBRB) system is proposed in this paper. The BBRB system consists of two-level belief rule bases, and the 2D and 3D characteristics of wear particles are used as antecedent attributes on each level. Quantitative and qualitative wear information with uncertainties can be processed simultaneously by the BBRB system. In order to enhance the efficiency of the BBRB, the silhouette value is adopted to determine referential points and the fuzzy c-means clustering algorithm is used to transform input wear information into belief degrees. In addition, the initial parameters of the BBRB system are constructed on the basis of expert-domain knowledge and then optimized by the genetic algorithm to ensure the robustness of the system. To verify the validity of the BBRB system, experimental data acquired from real-world diesel engines are analyzed. Five-fold cross-validation is conducted on the experimental data and the BBRB is compared with the other four models in the cross-validation. In addition, a verification dataset containing different wear particles is used to highlight the effectiveness of the BBRB system in wear mode identification. The verification results demonstrate that the proposed BBRB is effective and efficient for wear mode identification with better performance and stability than competing systems.