This chapter presents a novel approach for fault prediction in industrial systems, specifically designed to handle incomplete and missing data, which often undermine the accuracy of predictive models. The method leverages artificial intelligence to address incomplete information by dividing it into incomplete variables and missing variables, each managed with tailored solutions. A DBN is used to integrate complete data with uncertain information, effectively compensating for data integrity issues and reducing prediction errors. The approach also incorporates advanced techniques such as parameter uncertainty analysis, sensitivity analysis, and dynamic range analysis, enhancing the precision and comprehensiveness of predictions. The method’s effectiveness is demonstrated through its application to predicting CO2 corrosion in subsea pipelines, where it significantly improves fault prediction accuracy even in the presence of incomplete data. This approach sets a new benchmark in fault prediction, providing a reliable solution for industrial systems where incomplete data is a common challenge.