Abstract
This paper presents a novel fault detection method based on key variable forecasting models. The approach integrates future forecasts of key variables into a time window, allowing for early fault detection without modifying the offline training phase of the existing fault detection model. By incorporating predicted data into the detection process, the proposed method significantly improves fault detection rates and reduces detection delays. Experiments using the Continuous Stirred Tank Heater (CSTH) system demonstrate the superiority of our method over traditional approaches, showing the advantages of forecasting in enhancing detection performance. However, our results also highlight the dependency of the method's effectiveness on the quality of the forecasting model, suggesting the need for more advanced time-series forecasting techniques. Additionally, the current point forecasting method may not be sufficient in real-world applications, where probabilistic modeling of key variables would provide more reliable detection results. Future research directions will focus on refining forecasting models and exploring probabilistic approaches for more accurate fault detection.