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In the process industry, fault prediction and product-related fault monitoring are important links to ensure product quality and improve economic benefits. In this paper, under the framework of the BFGS (Broyden–Fletcher–Goldfarb–Shanno) algorithm, a new and more accurate data-driven method, the ABFGS algorithm, is proposed. Compared with the BFGS algorithm, the ABFGS algorithm adds output-related fault monitoring capabilities and has strong robustness, which can eliminate the influence of outliers on measurement data. The effectiveness of this method has been verified by the Eastman benchmark program in Tennessee. The simulation results show that this method can eliminate the influence of outliers and effectively predict the process. Compared with the other three algorithms, the ABFGS algorithm can not only clearly and accurately indicate whether the detected fault is related to the output but also provide a higher fault monitoring rate.

An Advanced Broyden–Fletcher–Goldfarb–Shanno Algorithm for Prediction and Output-Related Fault Monitoring in Case of Outliers