Abstract Multivariate statistical process monitoring (MSPM) has received book-length treatments and wide spread application in industry. In MSPM, multivariate data analysis techniques such as principal component analysis (PCA) are commonly employed to project the (possibly many) process variables onto a lower dimensional space where they are jointly monitored given a historical or specified reference set that is within statistical control. In this paper, PCA and biplots are employed together in an innovative way to develop an efficient multivariate process monitoring methodology for variable contribution identification and visualization. The methodology is applied to a commercial coal gasification production facility with multiple parallel production processes. More specifically, it is shown how the methodology is used to specify the optimal principal component combinations and biplot axes for visualization and interpretation of process performance, and for the identification of the critical variables responsible for performance deviations, which yielded direct benefits for the commercial production facility.

中文翻译：

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多元统计过程监测中的变量贡献识别和可视化

摘要 多变量统计过程监控（MSPM）在工业中得到了书本长度的处理和广泛的应用。在 MSPM 中，主成分分析 (PCA) 等多变量数据分析技术通常用于将（可能很多）过程变量投影到低维空间，在该空间中，在给定统计控制范围内的历史或指定参考集的情况下，对它们进行联合监测。在本文中，PCA 和双标图以一种创新的方式一起使用，以开发一种用于变量贡献识别和可视化的高效多变量过程监控方法。该方法应用于具有多个并行生产过程的商业煤气化生产设施。进一步来说，