Particle filtering for sensor fault diagnosis and identification in nonlinear plants
| Autor: | Predrag Tadic, Željko Ðurović |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
0209 industrial biotechnology
Engineering business.industry Process (computing) 02 engineering and technology Fault (power engineering) Industrial and Manufacturing Engineering Fault detection and isolation Computer Science Applications Nonlinear system 020901 industrial engineering & automation 13. Climate action Control and Systems Engineering Control theory Feature (computer vision) Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Calibration 020201 artificial intelligence & image processing business Particle filter Statistical hypothesis testing |
| Zdroj: | Journal of Process Control. 24:401-409 |
| ISSN: | 0959-1524 |
| DOI: | 10.1016/j.jprocont.2014.02.009 |
| Popis: | We propose a novel method for sensor monitoring and fault-tolerant estimation in systems described by general stochastic nonlinear and/or non-Gaussian state-space models. Faults are defined as abruptly occurring calibration errors, causing the sensor readings to be biased or scaled. Actuators and the process itself are assumed to be fault free. The main novelty of the work is an adaptive particle filter, whose configuration changes in order to diagnose sensor faults and to compensate for their effects. The presence, type and magnitude of sensor faults are determined through hypothesis testing and maximum likelihood estimation, based on the difference between the measurements and the particle filter estimates. The validity of the proposed approach was demonstrated through simulations on a drum-boiler model, although its effectiveness is not conditioned on any particular feature of the considered example. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect