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- W2768047177 abstract "Data-driven statistical process condition monitoring techniques have enjoyed remarkable growth over decades. However, although the source of data sets used for validating these monitoring algorithms may vary from computer simulations, experimental rigs and industrial processes, the fault being monitored are straightforward. Hence a benchmark data set acquired from an industrial-scale plant with complicated faults is of great interest. The multiphase flow facility presented in this work, which is a fully automated rig with adjustable configurations, can be a promising candidate. This paper applies a novel Multimode Kernel Latent Variable Canonical Variate Analysis (mKLV-CVA) algorithm to the benchmark data set collected from the multiphase flow facility in varying normal operating conditions as well as the slugging situation. Other multivariate statistical process monitoring techniques are also tested on the same data sets and their performances are compared through offline validations. In the meantime, this multiphase flow facility is proven to be promising as a benchmark industrial case study for validating the performance of data-driven process monitoring algorithms in unconventional faults." @default.
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- W2768047177 date "2017-01-01" @default.
- W2768047177 modified "2023-10-04" @default.
- W2768047177 title "Nonlinear Dynamic Process Monitoring: The Case Study of a Multiphase Flow Facility" @default.
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- W2768047177 doi "https://doi.org/10.1016/b978-0-444-63965-3.50251-8" @default.
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