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Multistate multivariate statistical process control
Author(s) -
Odom Gabriel J.,
Newhart Kathryn B.,
Cath Tzahi Y.,
Hering Amanda S.
Publication year - 2018
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2333
Subject(s) - principal component analysis , autocorrelation , outlier , fault detection and isolation , spurious relationship , computer science , nonlinear system , data mining , process (computing) , statistical process control , dimension (graph theory) , process state , anomaly detection , econometrics , statistics , artificial intelligence , mathematics , machine learning , physics , quantum mechanics , pure mathematics , actuator , operating system
Abstract 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.