基于独立成分分析及其扩展模型的工业过程监测方法综述

独立成分分析(independent component analysis,ICA)是一种多变量统计分析方法,常用于非高斯过程监测,它能够有效利用信号的高阶统计信息(三阶以上)提取相互独立的独立成分,在工业过程监测中得到了广泛的应用,是当前国际过程监测领域的研究热点.鉴于此,介绍经典ICA模型、改进ICA模型及其在工业...

Overview of industrial process monitoring methods based on independent component analysis and its extended model

The independent component analysis(ICA) is a multivariate statistical analysis method, which is often used for non-Gaussian process monitoring. It can effectively use the high-order statistical information (exceeding the third order) of the signal to extract independent components, which has been widely used in industrial process monitoring and is a research hotspot in the current international process monitoring field. As such, this paper introduces the classic ICA model, the improved ICA model and its process monitoring technology in industrial processes. Firstly, the classic ICA model is introduced, which is then classified and the advantages and disadvantages are pointed out. Secondly, in view of the shortcoming of the classic ICA model, the development of the improved ICA model is sorted out from three aspects, including ICA''s own problems, noise and outliers. Then, the industrial process is applied as the main application background, and the ICA process monitoring technology is evolved from simple to complex industrial processes. Faced with the single and mixed characteristics of operating data in the industrial process, the current research status of the ICA and its extended models in industrial process monitoring are reviewed. Finally, the problems to be solved in this research field and the future development directions are discussed.