Abstract/Summary

Industrial processes generally operate under multiple modes and a global monitoring approach, built upon combining local models which are aimed at each mode, requires complete data from all potential modes to be available. However, practical data are generated and collected in a steady stream, which makes it difficult if not impossible to process. This paper proposes an efficient sparse dynamic inner principal component analysis algorithm for multimode nonlinear dynamic process monitoring, which aims to build a single monitoring model with continual learning ability for successive modes. To reduce the storage and computational costs, only a few representative data from each mode are selected based on cosine similarity and replayed for retraining when a new mode arrives, which are sufficient to reflect the operating condition of each mode. Inspired by replay continual learning, data from all existing modes are preprocessed by its own statistics and then regarded as a whole data set, followed by building a single multimode monitoring model. The multimode dynamic latent variables are extracted from data in raw format, via a vector autoregressive model. Therefore, the proposed method is not constrained by the mode similarity, which makes it appropriate for diverse modes and convenient for long-term monitoring tasks. Besides, the proposed method can deal with nonlinearity and a regularization term is added to avoid the potential overfitting issue. Compared with state-of-the-art multimode monitoring methods, the effectiveness of the proposed approach is demonstrated by continuous stirred tank heater and a practical industrial system.