Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches —— Theory and Practical Applications

----- 使用高级数据驱动和深度学习方法进行统计过程监控：理论和实际应用

ISBN: 9780128193655 出版年：2020 页码：330 Harrou, Fouzi Sun, Ying Hering, Amanda S Elsevier Science_RM

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches 鈥?such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches 鈥?to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. Uses a data-driven based approach to fault detection and attributionProvides an in-depth understanding of fault detection and attribution in complex and multivariate systemsFamiliarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methodsIncludes case studies and comparison of different methods