GELU-LSTM-encoder-decoder fault prediction for batch processes based on the global-local percentile method

Fault prediction is a crucial strategy for ensuring the safety and efficiency of batch processes, and it aims to predict the degradation trend and isolate the fault. Operating mistakes or external environment changes can lead to the degradation of batch processes, eventually resulting in faults. The point at which degradation begins is known as the time to start prediction (TSP). Determining the TSP provides more reliable degradation information for fault prediction and enhances prediction accuracy. Nevertheless, it is difficult to obtain TSP in batch processes because of some complex data features, such as multi-phase and strong dynamics, which result in a significant loss of fault prediction. To address these challenges, a fault prediction method is proposed based on the global-local percentile method applied to Gaussian error linear unit-long short-term memory-encoder-decoder (GELU-LSTM-encoder-ED). Firstly, squared prediction error (SPE) is normalized to eliminate differences in data features. Secondly, a global-local percentile method is proposed to determine the TSP. The period is divided based on the threshold fluctuation to derive the model input. Then the global percentile model is employed to identify the interval to which the TSP belongs. Subsequently, the local percentile model is employed to analyze this interval and identify the TSP. Thirdly, the LSTM based on the encoder-decoder model, enhanced with the GELU, is utilized to improve the performance of fault prediction. Finally, two fault datasets on the penicillin fermentation process and Escherichia coli fermentation process are employed to demonstrate the performance of the presented method compared with other shared methods.