Mechanistic block‐based attention mechanism stacked autoencoder for describing typical unit connection industrial processes and their monitoring

Abstract

AbstractThe overall information of a process can be obtained through global modelling, and the local information is easily ignored in the research of the industrial process monitoring of unit connection. Thus, finding the global type of faults is easy, but occurs at the expense of drowning out the local faults. The use of block modelling can highlight local information, thereby improving local fault detection capability. However, the connection information between blocks is usually ignored in block modelling, which makes finding fault classes that only affect the connection relationship between blocks difficult. A mechanistic block‐based attention mechanism stacked autoencoder (MB‐AMSAE) monitoring method is proposed in this paper. The industrial process is divided into several parts in accordance with its mechanistic relationships, and each part represents an independent block. Self‐attention is used to focus on the information of each block itself. Cross‐attention is adopted to focus on the information between blocks, and this information is fused to form new blocks. The new block is used as the feature of the original block, and the original block is reconstructed by using a stacked autoencoder. The corresponding control limit is obtained in accordance with the reconstruction ability of normal samples, and whether the working conditions are normal is judged according to the control limit. The proposed algorithm is used in numerical simulation, Tennessee‐Eastman processes, and is compared with other advanced algorithms based on its fault detection capability. Results show the effectiveness of the MB‐AMSAE algorithm in process monitoring.