Model-based reliability evaluation of a common rail fuel system using one-dimensional physical model

Advancements in intelligent manufacturing critically depend on robust diagnostic methods for mechanical systems. In particular, enhancing the reliability of common rail fuel systems is vital for effective health management and maintenance of diesel engines. This study introduces a novel approach for comprehensive reliability assessment of common rail fuel systems. We have developed a one-dimensional physical model, validated through fuel cycle flow experiments. Failure modes are systematically identified using Failure Mode and Effects Analysis (FMEA), with typical fault conditions prioritized based on risk priority numbers for subsequent testing. Our experimental results validate the accuracy of the model's capability to simulate real-world failure scenarios. Utilizing the fault data generated by the model, we established a reliability assessment method that maps degradation fault trajectories within the system. By incorporating degradation failure theory, this method enables a system-wide evaluation of reliability. A case study employing the model's fault dataset demonstrates the method's effectiveness in improving reliability assessments and enhancing risk management, providing invaluable insights for optimizing health monitoring, operational, and maintenance strategies.