Process monitoring method based on vine copula and transfer learning strategy

In practical industrial processes, the limited sampling time and other factors result in a scarcity of historical data for certain modes, leading to diminished generalization and accuracy of process monitoring models. To solve this problem, a process monitoring method based on vine copula-based dependence description (VCDD) and transfer learning strategy (TLVCDD) is proposed in this study. The proposed method constructs a VCDD model by utilizing target domain data and subsequently selects suitable candidate sample groups from the abundant source domain data based on this model. Candidate sample groups are transferred sequentially according to their priorities, which are quantified based on the maximum mean discrepancies between candidate sample groups and the target domain data. The VCDD model exhibiting the most superior overall performance during the transfer process is chosen and employed for online process monitoring. The effectiveness of the proposed method is demonstrated through a numerical example and Tennessee Eastman (TE) process.