Multistate multivariate statistical process control
Multistate multivariate statistical process control
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DOI：
10.1002/asmb.2333
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发表时间：
2018-11
影响因子：
1.4
通讯作者：
Gabriel J. Odom;Kathryn B. Newhart;T. Cath;A. Hering
中科院分区：
文献类型：
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作者：
Gabriel J. Odom;Kathryn B. Newhart;T. Cath;A. Hering
对于高维、自相关、非线性和非平稳数据，自适应动态主成分分析（AD‐PCA）在标记离群值方面表现得与非线性降维方法一样好或更好。在一些工程系统中，设计的功能可以在多个自相关，非线性和非平稳过程中创建一个已知的多状态方案，并将这些额外的已知信息纳入AD‐PCA可以进一步改善它。在模拟中引入三种类型的故障之一，我们比较了考虑状态与忽略它们。我们发现，多状态AD-PCA降低了虚警的比例，减少了故障检测的平均时间。相反，我们还研究了假设多个状态时，只有一个存在的影响，并发现，只要观察的数量是足够的，这种错误的规格是无害的。然后，我们将多态AD-PCA应用于从分散式污水处理系统在控制和失控条件下收集的真实的世界数据。多状态AD‐PCA比其单状态竞争对手更早、更一致地标记强系统故障。此外，当过程处于控制中时，考虑物理交换系统不会增加假警报的数量，并且最终可以帮助故障归因。
For high‐dimensional, autocorrelated, nonlinear, and nonstationary data, adaptive‐dynamic principal component analysis (AD‐PCA) has been shown to do as well or better than nonlinear dimension reduction methods in flagging outliers. In some engineered systems, designed features can create a known multistate scheme among multiple autocorrelated, nonlinear, and nonstationary processes, and incorporating this additional known information into AD‐PCA can further improve it. In simulations with one of three types of faults introduced, we compare accounting for the states versus ignoring them. We find that multistate AD‐PCA reduces the proportion of false alarms and reduces the average time to fault detection. Conversely, we also investigate the impact of assuming multiple states when only one exists, and find that as long as the number of observations is sufficient, this misspecification is not detrimental. We then apply multistate AD‐PCA to real‐world data collected from a decentralized wastewater treatment system during in control and out of control conditions. Multistate AD‐PCA flags a strong system fault earlier and more consistently than its single‐state competitor. Furthermore, accounting for the physical switching system does not increase the number of false alarms when the process is in control and may ultimately assist with fault attribution.