Stochastic configuration networks (SCNs) are widely used in the field of soft sensor modeling due to their advantages of good generalization performance and automatic model structure determination. The classical SCN-based soft sensors are usually effective when industrial processes only involve a single operation mode. In practical applications, however, operation mode variations are often seen because of many factors, including market demands, raw material changes, ambient temperatures, etc. In the historical modes, abundant labeled samples are collected. In the new operation mode, the labeled samples are, however, very scarce and cannot sufficiently support the effective training of soft sensor models. How to make full use of the historical modes to assist the soft sensor modeling of the new mode is, therefore, a meaningful and challenging problem. To handle this problem, this article proposes a transfer learning soft sensor modeling method based on 2-D domain-adaption SCN (TD-DASCN). In this method, a domain adaption SCN modeling framework is designed for transfer learning soft sensor development by fusing the abundant labeled samples from historical modes (source domain) and a few labeled samples from new modes (target domain). The feature alignment procedure is performed by using geodesic flow kernel method to reduce data distribution difference between source and target domains. For the sake of avoiding the possible negative transfer phenomenon, the source domain loss function is constrained according to the degree of contribution of the source domain samples in the transfer. Last, the effectiveness of the proposed method is verified by two industrial cases. Compared with the basic DASCN soft sensor method, the proposed method can reduce the average prediction RMSE value by 30.0% and 9.1% in the two tested cases, respectively.
