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 squaresmethods. The residual between processmeasurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-drivenmodel 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.
least squaresmethods. The residual between processmeasurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-drivenmodel 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.
Original language | English |
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Journal | IEEE Transactions on Energy Conversion |
Volume | 23 |
Issue number | 2 |
Pages (from-to) | 659-668 |
Number of pages | 10 |
ISSN | 0885-8969 |
DOIs | |
Publication status | Published - 2008 |
Keywords
- Fault Detection
- Data-driven Detection
- Power plants
- Unknown input observer