A Review of Generalized Likelihood Ratio Based Monitoring Techniques
- Type: Conference Presentation
- Conference Type: AIChE Spring Meeting and Global Congress on Process Safety
- Presentation Date: April 24, 2018
- Duration: 30 minutes
- PDHs: 0.50
Unfortunately, most of the currently available fault detection methods in literature utilize a GLR chart designed specifically to detect only shifts in the mean. However, in practice an industrial process can experience a wide variety of fault types, i.e., shifts in the variance, and drifts. The GLR charts designed specifically for different scenarios, i.e., to detect shifts in the mean, and to detect shifts in the variance, have shown that they are capable of efficiently detecting the different classes of faults, and that they provide extremely low missed detection rates, and out-of-control average run length (ARL1) values even for small fault magnitudes [3,4].
Exponential Likelihood Ratio (ELR) charts were developed in order to enhance the performance of the GLR chart by incorporating exponential weights in order to obtain a more accurate estimate of the current state of the process . Like the GLR charts, the ELR charts can also be designed to detect different classes of faults. However, the performance of the ELR charts are tied to the choice of its smoothing parameter, and therefore one objective of this work is to provide an algorithm to select the appropriate value of the smoothing parameter for monitoring purposes.
Other exponentially weighted GLR techniques exist . Unfortunately a comparison and review of the performance of all likelihood-based monitoring techniques is unavailable in literature. Therefore, another objective of this work is to compare the performance of all likelihood-based monitoring techniques, in order to conclude which chart provides the best monitoring performance. Illustrative examples, such as the Tennessee Eastman Process will be utilized in order to evaluate the performances of the different techniques.
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