Continual Learning-Based Probabilistic Slow Feature Analysis for Monitoring Multimode Nonstationary Processes

A novel continual learning-based probabilistic slow feature analysis algorithm is introduced for monitoring multimode nonstationary processes. Multimode slow features are extracted and an elastic weight consolidation (EWC) is adopted for sequential modes. EWC was originally introduced in the setting of machine learning of sequential multi-tasks with the aim of avoiding catastrophic forgetting issue, which equally poses as a major challenge in multimode nonstationary process monitoring. When a new mode arrives, a small set of data are collected for continual learning by the proposed algorithm. A regularization term is introduced to prevent new data from significantly interfering with the learned knowledge, where the parameter importance measures are estimated. The proposed method is referred to as PSFA–EWC, which is updated continually and is capable of achieving excellent performance. PSFA–EWC furnishes backward and forward transfer ability by a single model. The significant features of previous modes are retained while consolidating new information, which may contribute to learning new relevant modes. The effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system. Note to Practitioners—Since industrial systems operate in varying modes and data are nonstationary within each mode, multimode nonstationary process monitoring is increasingly important. Traditional multimode monitoring methods generally need complete data from all possible modes and may need to be retrained from scratch when a new mode arrives, which require expensive computation and storage resources. Besides, it is difficult to distinguish real faults from normal variations in multimode nonstationary processes. This paper proposes a novel continual learning-based probabilistic slow feature analysis, where elastic weight consolidation is employed to consolidate the previously learned knowledge while extracting multimode slow features. The monitoring model is updated sequentially and provides backward as well as forward transfer learning ability for successive modes. It is able to separate real faults from normal dynamics, which is beneficial to identifying a new mode for multimode nonstationary processes. In addition, the proposed approach delivers excellent model interpretability and deals with missing data as well as uncertainty. In industrial applications, such as power plants and intelligent manufacturing processes, the proposed method can provide excellent monitoring performance.