Historical Information-Aided Monitoring of Few-Sample Modes in Industrial Processes With Orthogonal Transferred Projection

Few-sample modes are easy to appear when a new working condition is triggered in industrial processes especially during the early stages of the new working mode. However, monitoring the early behavior of a new mode is important because engineers and operators are less knowledgeable with such a new mode. Considering the few-sample challenge in this problem, a new multisource transfer learning framework is proposed that leverages historical data under various operating conditions to enrich process monitoring over new mode data. In contrast to existing transfer learning-related work, a new unsupervised domain adaptation framework is designed. The historical modes as the source provide precious knowledge and reference to the new mode so that the features of the new mode are robust to noise and insufficient samples. Mathematically, the historical features play the role of a regularizer for the feature learning in the target domain. A geometrical illustration is given and an iterative optimization algorithm is developed with the convergence analysis. Except for the features guided by historical modes, individual features of the new mode are also extracted from the residual part to form a complete monitoring framework. Finally, the effectiveness of the proposed method is validated through a numerical experiment and a real industrial hydrocracking process.