A Reliability Fault Diagnosis Method for Diesel Engines Based on the Belief Rule Base with Data-Driven Initialization

Diesel engines serve as critical power sources across transportation and industrial fields, and their fault diagnosis is essential for ensuring operational safety and system reliability. However, acquiring sufficient and effective operational data remains a significant challenge due to the high complexity of the systems. As a modeling method that incorporates expert knowledge, the belief rule base (BRB) demonstrates strong potential in resolving such challenges. Nevertheless, the reliance on expert knowledge constrains its practical application, particularly in complex engineering scenarios. To overcome this limitation, this study proposes a reliability fault diagnosis method for diesel engines based on the belief rule base with data-driven initialization (DI-BRB-R), which aims to improve modeling capability under conditions of limited expert knowledge. Specifically, the approach first employs fuzzy c-means clustering with the Davies-Bouldin index (DBI-FCM) to initialize attribute reference values. Then, a Gaussian membership function with Laplace smoothing (LS-GMF) is developed to initialize the rule belief degrees. Furthermore, to guarantee the reliability of the model optimization process, a group of reliability guidelines is introduced. Finally, the effectiveness of the proposed method is validated through an example of fault diagnosis of the WD615 diesel engine.