(60dc) Monitoring Methods for Fault Detection and Diagnosis: Algorithms and Applications
- Conference: AIChE Spring Meeting and Global Congress on Process Safety
- Year: 2020
- Proceeding: 2020 Virtual Spring Meeting and 16th GCPS
- Group: Spring Meeting Poster Session and Networking Reception
- Time: Wednesday, August 19, 2020 - 3:00pm-4:00pm
A number of algorithms have been developed in order to improve existing fault detection and diagnosis performance. These algorithms integrate a number of different data-driven driven tools and methods. Multiscale wavelet-based representation of data can be used in order to handle data that is autocorrelated, non-Gaussian, and noisy. Hypothesis testing methods such as the Generalized Likelihood Ratio (GLR) technique can be used in order to provide the best possible detection for a fixed false alarm rate. Moreover, certain model-based methods have been developed in order to monitor process drifts and degradations in the process model, even when a process is operating under control.
This work will discuss the different tools utilized in development of the algorithms, their features, and practical applications, including the benchmark Tennessee Eastman Process (TEP).
This paper has an Extended Abstract file available; you must purchase the conference proceedings to access it.
Do you already own this?
Log In for instructions on accessing this content.
|Employees of CCPS Member Companies||$150.00|
|AIChE Graduate Student Members||Free|
|AIChE Undergraduate Student Members||Free|