Chemical process time series prediction using multi‐source transfer learning based on <scp>ESN</scp>

Abstract When incipient fault data of chemical processes is insufficient, transfer learning enables fault degradation trend prediction and process safety improvement. Traditional echo state network (ESN)‐based transfer learning generally utilizes a single source domain, while traditional multi‐source domain selection methods do not consider information complementation between source domains. Therefore, a multi‐source transfer learning based on ESN (MTL‐ESN) is proposed to improve chemical process time series prediction performance. In order to consider information complementation between source domains, a similar‐different source domain selection strategy is designed. To comprehensively utilize the transfer knowledge of multi‐source domains, transfer ESN sub‐models are established for selected source domains, and integrated to an ensemble ESN prediction model. For faults, especially mixed fault operating conditions, the proposed MTL‐ESN can select source domains correctly and predict variable trend accurately. Simulations on a continuous stirred tank reactor demonstrate the effectiveness of the proposed time series prediction method.