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Skip to Search Results- 4Process Monitoring
- 2Fault Detection
- 1Alarm Monitoring
- 1Bayesian Inference
- 1Computational Geometry
- 1Conditional Random Field
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Fall 2016
Nowadays, industrial processes are becoming highly complex and integrated due to the applications of advanced distributed control systems. As multiple production units with thousands of actuators are operating at the same time, the reliability issue of process plants naturally arises. To ensure...
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Enhanced Probabilistic Slow Feature Analysis - Dealing with Complexities in Industrial Process Data
DownloadFall 2023
In modern industrial processes, the measurement and storage of thousands of correlated process variables have become commonplace. Dimensionality reduction techniques are often employed to extract underlying informative patterns called features by discarding redundant information. Slow feature...
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Hierarchical Monitoring and Probabilistic Graphical Model Based Fault Detection and Diagnosis
DownloadFall 2020
As the rapid development of modern industry, data based fault detection and diagnosis for industrial processes have become increasingly critical to ensure process safety and product quality. To effectively make use of underlying features of process data, multiple data based fault detection and...
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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...