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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Miranda, M. Edelmayer, A. |
| Copyright Year | 2012 |
| Description | Author affiliation: Laboratory of Unit Operations, Department of Chemical Engineering, Faculty of Engineering, University of Los Andes, 5101, Mérida, Venezuela (Miranda, M.) || Systems and Control Laboratory, Computer and Automation Research Institute, Hungarian Academy of Sciences, H-1111, Budapest, Kende u. 13-17, Hungary (Edelmayer, A.) |
| Abstract | This paper presents a dual layer approach for robust fault tolerant estimation of nonlinear processes using a combined adaptive extended Kalman filter and fault detection and filter reconfiguration. From the one hand, the filter is made robust in face of environment uncertainty using adaptive filtering. To this end, the filter identifies the measurement covariance by means of recursive estimation, upon which the adaptation relies, to suppress the effect of sporadic variations in the quality of measurements as well as compensates for incipient sensor faults. From the other hand, fault monitoring is continuously applied to the filter's innovation in an attempt to initiate filter reconfiguration when the adaptation mechanism alone is not able to overcome the failure situation. The discussion of the results is embedded in the application framework of state estimation of a batch distillation process. |
| Starting Page | 703 |
| Ending Page | 709 |
| File Size | 618789 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781467325301 |
| e-ISBN | 9781467325318 |
| e-ISBN | 9781467325295 |
| DOI | 10.1109/MED.2012.6265720 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-07-03 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Temperature measurement Technological innovation Fault tolerance Noise Fault tolerant systems Estimation Mathematical model |
| Content Type | Text |
| Resource Type | Article |
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