A Systematic Comparison of PCA-based Statistical Process Monitoring Methods for High-dimensional, Time-dependent Processes
A Systematic Comparison of PCA-based Statistical Process Monitoring Methods for High-dimensional, Time-dependent Processes
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DOI：
10.1002/aic.15062
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发表时间：
2016-05-01
期刊：
影响因子：
3.7
通讯作者：
De Ketelaere, Bart
中科院分区：
文献类型：
--
作者：
Rato, Tiago;Reis, Marco;De Ketelaere, Bart
高维和时间相关的数据对统计过程监控提出了重大挑战。大多数高维方法来科普这些挑战依赖于某种形式的主成分分析（PCA）模型，通常分为非自适应和自适应。非自适应方法包括静态PCA方法和动态主成分分析（DPCA）的数据与自相关。方法，如DPCA与去相关残差，扩展DPCA，以进一步减少自相关和互相关对监测统计量的影响。递归主成分分析和移动窗口主成分分析是为非平稳数据而发展起来的，具有自适应性。这些基本方法将系统地比较高维，时间依赖的过程（包括田纳西州伊士曼基准过程），为从业人员提供适当的监测策略和他们如何可以预期执行的感觉的指导方针。还讨论了不同方法的参数值的选择。最后，时间相关的数据建模的相关挑战进行了讨论，并强调可能进一步研究的领域。(C)2016年美国化学工程师学会
High-dimensional and time-dependent data pose significant challenges to Statistical Process Monitoring. Most of the high-dimensional methodologies to cope with these challenges rely on some form of Principal Component Analysis (PCA) model, usually classified as nonadaptive and adaptive. Nonadaptive methods include the static PCA approach and Dynamic Principal Component Analysis (DPCA) for data with autocorrelation. Methods, such as DPCA with Decorrelated Residuals, extend DPCA to further reduce the effects of autocorrelation and cross-correlation on the monitoring statistics. Recursive Principal Component Analysis and Moving Window Principal Component Analysis, developed for nonstationary data, are adaptive. These fundamental methods will be systematically compared on high-dimensional, time-dependent processes (including the Tennessee Eastman benchmark process) to provide practitioners with guidelines for appropriate monitoring strategies and a sense of how they can be expected to perform. The selection of parameter values for the different methods is also discussed. Finally, the relevant challenges of modeling time-dependent data are discussed, and areas of possible further research are highlighted. (C) 2016 American Institute of Chemical Engineers