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• W1167511737 abstract "Multivariate statistical process control (MSPC) is an important means of monitoring multiple process variables and their interrelationships while controlling chemical and industrial plants efficiently and stably. To consider nonlinearities between process variables and multimodal data distributions, the data density can be used as an index for fault detection. Data domains with a low data density are considered abnormal states. However, after fault detection, faulty process variables cannot be diagnosed with an MSPC model based on the data density. Therefore, we have developed a new index to diagnose the process variables that contribute to process faults using a data density-based MSPC model. The proposed index uses the partial derivative of an MSPC model with respect to each process variable. We demonstrate the effectiveness of the proposed method using numerical simulation data, Tennessee Eastman process data, and real plant data analyses." @default.
• W1167511737 created "2016-06-24" @default.
• W1167511737 creator A5082283686 @default.
• W1167511737 creator A5087461964 @default.
• W1167511737 date "2015-10-01" @default.
• W1167511737 modified "2023-10-16" @default.
• W1167511737 title "Data density-based fault detection and diagnosis with nonlinearities between variables and multimodal data distributions" @default.
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• W1167511737 doi "https://doi.org/10.1016/j.chemolab.2015.07.016" @default.
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