An efficient multimodal attentional principal component analysis for continual learning-based dynamic process monitoring

Abstract

Traditional multimode process monitoring methods extract features from time series data. Due to the catastrophic forgetting effect, data-driven multimode dynamic process monitoring is challenging based on a single monitoring model paradigm, i.e. the learned knowledge from previous modes may diminish as operating conditions undergo changes between modes, yet it is impractical to access all past data to retrain the model. In this work, a novel efficient method of multimodal attentional principal component analysis (M-APCA) with continual learning ability is introduced. Under the assumption that data from successive modes are received sequentially, dynamic process data are modeled using an attention mechanism to capture the relationship between data and the latent space, whereby meaningful information is concentrated as dynamic features which are extracted via a vector autoregressive model. In order to overcome the catastrophic forgetting problem, the idea of replay continual learning is employed. Specifically, past modes’ data which are significant to reflect the operating conditions, are selected and stored. These are repeatedly used in tandem with sequential data as replay data. Two types of attention mechanisms are considered and analyzed, each of which is specifically designed to learn from data in an unsupervised manner, so the overall algorithm is efficient both in time and storage costs. The proposed attentional principal component analysis and M-APCA are analyzed against several state-of-the-art methods to highlight the virtues of the proposed method. Compared with multimode monitoring methods, the effectiveness is demonstrated through case studies of: a continuous stirred tank heater, the Tennessee Eastman process and a practical coal pulverizing system.