Artificial intelligence enhanced fault prediction with industrial incomplete information

With the rapid advancement of sensor and information technology, fault prediction for industrial equipment has become increasingly feasible. However, accurate fault prediction is heavily dependent on the integrity of monitoring data. Unfortunately, limitations in data collection and storage technologies often result in incomplete and missing data, leading to decreased accuracy or even failure of predictive models. To address this challenge, this paper introduces a novel dataenhanced fault prediction method leveraging artificial intelligence, which stands out for its ability to handle incomplete information effectively. The innovation lies in the first-time division of incomplete information into incomplete variables and missing variables, each addressed with tailored AI-based solutions. Furthermore, the method integrates complete data with uncertain information through a dynamic Bayesian network, pioneering a new approach to compensating for data integrity issues and minimizing prediction errors. The enhanced data is then utilized for long-term performance predictions, setting a new standard in fault prediction accuracy. The developed model exhibits exceptional adaptability by integrating advanced techniques such as parameter uncertainty analysis, sensitivity analysis, and dynamic range analysis. These enhancements significantly boost the precision and comprehensiveness of fault predictions. The method's feasibility and superiority are demonstrated through the prediction of CO2 corrosion in subsea pipelines, proving that the proposed approach significantly improves fault prediction accuracy even under incomplete information conditions.