In this paper, a novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) in order to extract multimode slow features for online monitoring. 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 dynamic process monitoring. When a new mode arrives, a set of data should be collected so that this mode can be identified by PSFA and prior knowledge. Then, 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 denoted as PSFA-EWC, which is updated continually and capable of achieving excellent performance for successive modes. Different from traditional multimode monitoring algorithms, PSFA-EWC furnishes backward and forward transfer ability. The significant features of previous modes are retained while consolidating new information, which may contribute to learning new relevant modes. Compared with several known methods, the effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system.
Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校机械科学与工程系）；Coordinated Science Laboratory, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校协调科学实验室）；Department of Mechanical Engineering, University of Michigan（密歇根大学机械工程系）
Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Texas at El Paso（德克萨斯大学埃尔帕索分校）；National Center for Supercomputing Applications（国家超级计算应用中心）
More Sensors Only One Field: Rethinking Continual Spatio-Temporal Forecasting
City University of Hong Kong (Dongguan)（香港城市大学（东莞））；City University of Hong Kong（香港城市大学）；Zhuhai College of Science and Technology（珠海科技学院）；Northwestern Polytechnical University（西北工业大学）
FastODT: A tree-based framework for efficient continual learning
Masaryk University（马萨里克大学）；Czech Republic（捷克共和国）；Warsaw University of Technology（华沙技术大学）；Poland（波兰）；Mycroft Mind；New York University（纽约大学）；New York, NY, USA（纽约市，纽约州，美国）；Analog AI；Abu Dhabi, UAE（阿布扎赫，阿联酋）
Characterizing Continual Learning Scenarios and Strategies for Audio Analysis
Indian Institute of Technology Ropar（印度理工学院罗帕尔分校）；University of Regensburg（雷根斯堡大学）；Inception Institute of AI（Inception人工智能研究院）；Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；University of Ottawa（渥太华大学）
Gradient-Free Continual Learning in Spiking Neural Networks via Inter-Spike Interval Regularization
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Indian Institute of Technology Delhi（印度理工学院德里）；University of Texas at El Paso（得克萨斯大学埃尔帕索分校）；National Center for Supercomputing Applications（国家超级计算应用中心）
A Conformal Predictive Measure for Assessing Catastrophic Forgetting
Communication Systems Department, EURECOM, France（EURECOM通信系统部）；Andalusian Institute of Data Science and Computational Intelligence (DaSCI)（安达卢西亚数据科学与计算智能研究所）；Department of Computer Science and Artificial Intelligence, University of Granada, Spain（格拉纳达大学计算机科学与人工智能系）
WARD: Runtime Workload-Adaptive Vision TRansformer Framework for Dependable Edge AI
Humboldt University of Berlin（柏林洪堡大学）；Tallinn University of Technology（塔林理工大学）；Brandenburgische Technische Universität Cottbus-Senftenberg（科特布斯-森夫滕贝格勃兰登堡工业大学）；Shahid Bahonar University of Kerman（克尔曼沙希德巴霍纳尔大学）
