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Fall 2014
While there has been much literature in the area of system monitoring and diagnosis, most of these techniques have a relatively small scope in terms of the faults and performance issues that they are built to detect. When implementing several monitors simultaneously on a single process, a single...
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Monitoring of Industrial Processes via Non-stationary Probabilistic Slow Feature Analysis Machine Learning Algorithm
DownloadSpring 2020
This research develops a first of its kind machine learning (ML) algorithm, called probabilistic slow feature analysis (PSFA), that monitors and detects faults for non-stationary industrial processes. The novelty of this ML algorithm is that it can monitor and detect faults for non-stationary...
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Probabilistic Graphical Models for Data Reconciliation and Causal Inference in Process Data Analytics
DownloadSpring 2021
Data reconciliation methods play an important role in minimizing the measurement error and gross error that are present in the process data with respect to the process model. On the other hand, causal analysis helps in determining the relationship among the process variables from the data. It is...