Health Assessment Method for EWIS Based on Bayesian Inference and Expert Knowledge

With the increasing complexity of modern aircraft, the Electrical Wiring Interconnection System (EWIS) has become a critical component in ensuring flight safety and system reliability. However, due to prolonged exposure to harsh environments such as high temperatures, vibrations, humidity, and electromagnetic interference, the components of EWIS systems are prone to aging, wear, and insulation degradation, which pose serious threats to system performance and aviation safety. Although existing health assessment methods have made some progress, these methods often rely on large-scale or high-precision data that are difficult to obtain, or are limited to a single source of information, restricting their applicability. To address these issues, this paper proposes a health assessment method for EWIS based on the integration of Bayesian inference and expert knowledge. Under conditions of small sample sizes, this method combines multi-source data with expert knowledge to manage uncertainty, conduct causal reasoning, and provide robust health status predictions. The study shows that this method improves the accuracy, interpretability, and adaptability of health assessments, provides scientific decision support for aircraft maintenance, and offers both theoretical and practical value for the health management and predictive maintenance of other high-risk complex systems.