INSPECTION AND MAINTENANCE SCHEME OPTIMIZATION CONSIDERING ATMOSPHERIC CORROSION USING BAYESIAN NETWOR

Abstract

Atmospheric corrosion is a significant challenge the aviation industry faces, as it greatly affects the structural integrity of aircraft operated long after introduction. Therefore, an appropriate corrosion deterioration model is required to predict corrosion problems. However, the deterioration model is challenging to use in practice due to the limited data available for parameter estimation; thus, high uncertainty in prediction is unavoidable. To address these challenges, a method of integrating a physics-based model and a data-driven model on a Bayesian network (BN) is presented. The atmospheric corrosion environment is modeled using COMSOL, and a BN is constructed. Model calibration is performed using the collected atmospheric corrosion monitoring data at aircraft parking areas. The calibration approach improves upon existing models by incorporating actual environmental data, making it more accurate and applicable to real-world scenarios. Using the calibrated model, a method for optimizing the inspection and maintenance (I&M) scheme is described. In conclusion, our research emphasizes the importance of precise corrosion models for predicting and managing atmospheric corrosion in aircraft structures. BN that integrates physics-based and experimental monitoring data can improve the accuracy and applicability of these models, ensuring the safety and structural integrity of aircraft. And also, the results open up new avenues for future research, such as incorporating additional data sources to improve the accuracy of corrosion models further.