Abstract Multivariate data analysis is a powerful tool for process monitoring and data analysis. The theoretical methodology of real-time multivariate data analysis has been studied in the last decade. However, the effect of dataset size on modeling structure and fault detection ability has not been reported yet. In this paper, requirements for a minimum dataset for multivariate data analysis modeling are studied, and a practical approach is provided to evaluate the modeling structure. A method based on statistical index g2 and cross-validation is proposed to determine a minimum dataset size of a valid model for statistical process monitoring. The proposed method was built on the linear PLS model and elaborated by case studies using both batch and continuous processes. This paper provides theoretical development of multivariate data analysis and demonstrates its application in chemical processes.

中文翻译：

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数据集大小对化学过程建模和监测的影响

摘要 多元数据分析是过程监控和数据分析的有力工具。实时多元数据分析的理论方法已经在过去十年中得到了研究。然而，数据集大小对建模结构和故障检测能力的影响尚未见报道。本文研究了多元数据分析建模对最小数据集的要求，并提供了一种实用的方法来评估建模结构。提出了一种基于统计指标g2和交叉验证的方法来确定用于统计过程监控的有效模型的最小数据集大小。所提出的方法建立在线性 PLS 模型上，并通过使用批处理和连续过程的案例研究进行阐述。