Early fault diagnosis in chemical processes through multistep multivariable prediction

Effective fault detection and diagnosis (FDD) in chemical process systems is critical for maintaining safe and reliable operations. While deep learning methods have improved fault classification performance, they often require long sequences of data after a fault occurs, delaying timely interventions. In this work, we propose an early fault diagnosis method that enables rapid fault diagnosis using multistep multivariable prediction. Our approach employs a transformer-based prediction model to predict future values of key process variables, enriching the current data with these predictions. An LSTM-based model then classifies the enriched data into specific fault categories, leveraging both current and predicted information for improved precision. We evaluate the performance of the proposed approach on the Tennessee Eastman Process benchmark, demonstrating its Effectiveness in early fault diagnosis.