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- W4384345523 abstract "Fault detection in multi-rate process systems is a challenging task. Common techniques used for fault detection include threshold-based detectors, statistical detectors, and machine learning-based detectors. One such statistical detector technique is multiple probabilistic principal component analysis (MPPCA). MPPCA uses probabilistic PCA to detect fault signals from multiple sensors without down-sampling or up-sampling. This paper uses MPPCA to detect faults in a two-phase reactor–condenser system with recycle (TPRCR) with three measurement classes. These measurement data are used to build the MPPCA model using expectation maximization (EM). Based on this, T2 and SPE statistics are generated for fault detection in TPRCR systems, and the MPPCA approach’s effectiveness for fault detection is satisfactory." @default.
- W4384345523 created "2023-07-15" @default.
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- W4384345523 date "2023-05-17" @default.
- W4384345523 modified "2023-10-18" @default.
- W4384345523 title "Fault Detection of Multi-Rate Two Phase Reactor Condenser System with Recycle Using Multiple Probabilistic Principal Component Analysis" @default.
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- W4384345523 doi "https://doi.org/10.3390/ecp2023-14669" @default.
- W4384345523 hasPublicationYear "2023" @default.
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