Observer and data-driven-model-based fault detection in power plant coal mills
Abstract
This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault caused by a blocked inlet pipe. All three approaches detect the fault as it emerges. The optimal unknown input observer approach is most robust, in that, it has no false positives. On the other hand, the data-driven approaches are more straightforward to implement, since they just require the selection of appropriate confidence limit to avoid false detection. The proposed hybrid approach is promising for systems where a firstmore »
- Authors:
-
- University of Aalborg, Aalborg (Denmark). Dept. of Electrical Systems
- Publication Date:
- OSTI Identifier:
- 21059027
- Resource Type:
- Journal Article
- Journal Name:
- IEEE Transactions on Energy Conversion
- Additional Journal Information:
- Journal Volume: 23; Journal Issue: 2; Journal ID: ISSN 0885-8969
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 01 COAL, LIGNITE, AND PEAT; MILLING MACHINES; PULVERIZERS; FOSSIL-FUEL POWER PLANTS; COAL; DEFECTS; DETECTION; ENERGY BALANCE; NUMERICAL SOLUTION; REGRESSION ANALYSIS
Citation Formats
Odgaard, P F, Lin, B, and Jorgensen, S B. Observer and data-driven-model-based fault detection in power plant coal mills. United States: N. p., 2008.
Web. doi:10.1109/TEC.2007.914185.
Odgaard, P F, Lin, B, & Jorgensen, S B. Observer and data-driven-model-based fault detection in power plant coal mills. United States. https://doi.org/10.1109/TEC.2007.914185
Odgaard, P F, Lin, B, and Jorgensen, S B. 2008.
"Observer and data-driven-model-based fault detection in power plant coal mills". United States. https://doi.org/10.1109/TEC.2007.914185.
@article{osti_21059027,
title = {Observer and data-driven-model-based fault detection in power plant coal mills},
author = {Odgaard, P F and Lin, B and Jorgensen, S B},
abstractNote = {This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault caused by a blocked inlet pipe. All three approaches detect the fault as it emerges. The optimal unknown input observer approach is most robust, in that, it has no false positives. On the other hand, the data-driven approaches are more straightforward to implement, since they just require the selection of appropriate confidence limit to avoid false detection. The proposed hybrid approach is promising for systems where a first principles model is cumbersome to obtain.},
doi = {10.1109/TEC.2007.914185},
url = {https://www.osti.gov/biblio/21059027},
journal = {IEEE Transactions on Energy Conversion},
issn = {0885-8969},
number = 2,
volume = 23,
place = {United States},
year = {Sun Jun 15 00:00:00 EDT 2008},
month = {Sun Jun 15 00:00:00 EDT 2008}
}