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Chemical processes are being more heavily instrumented leading to larger and larger number of variables to monitor. Developing models for process monitoring using this colossal amount of data in a high dimensional space of process variables is a difficult task. In the recent times, immune system inspired Negative Selection Algorithm (NSA) has been gaining much attention for fault detection. Generally,...
As process industries grow in complexity, detection, diagnosis and correction become more and more difficult owing to the large number of variables that have to be monitored. Many trouble shooting problems can be effectively handled by monitoring only a small subset of the entire set of process variables. In this paper we propose a metric based on Bhattacharyya distance to measure the extent of similarity...
In a large-scale complex chemical process, hundreds of variables are measured. Since statistical process monitoring techniques such as PCA typically involve dimensionality reduction, all measured variables are often provided as input without pre-selection of variables. In our previous work [1], we demonstrated that reduced models based on only a small number of important variables, called key variables,...
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