Particle filtering for sensor fault diagnosis and identification in nonlinear plants |
| |
Affiliation: | 1. School of Telecommunication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;2. School of Electronics Engineering and Computer Science, Queen Mary University of London, Lordon, UK;1. University of Lorraine, CRAN UMR CNRS 7039, 54400 Cosnes et Romain, France;2. EPI Inria DISCO, Laboratoire des Signaux et Systèmes, CNRS-CentraleSupélec, 91192 Gif-sur-Yvette, France;3. Laboratory for Innovations in Sensing, Estimation, and Control, Department of Mechanical Engineering, University of Minnesota, Minneapolis, USA;4. Department of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand;5. Einderpad, 20A, 3920 Lommel, Belgium |
| |
Abstract: | 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. |
| |
Keywords: | Fault detection and isolation Sensor health monitoring Fault-tolerant estimation Particle filter Dedicated observer Generalized maximum likelihood rule |
本文献已被 ScienceDirect 等数据库收录! |
|