A Reliable Fault Diagnosis Method Based on Belief Rule Base Consider Robustness Analysis
Researchers have developed a new fault diagnosis method for equipment that enhances the robustness of belief rule base (BRB) models. This approach addresses issues with sensor data reliability by systematically assessing and optimizing the BRB model's robustness. The method was validated using examples of diesel engine and bearing fault diagnoses, demonstrating improvements in both accuracy and robustness. AI
IMPACT Enhances equipment safety and operational efficiency through improved fault diagnosis.