FDI by extended Kalman filter parameter estimation for an industrial actuator benchmark

✍ Scribed by B.K. Walker; Kuang-Yang Huang

• Book ID: 103988789
• Publisher: Elsevier Science
• Year: 1995
• Tongue: English
• Weight: 585 KB
• Volume: 3
• Category: Article
• ISSN: 0967-0661
• DOI: 10.1016/0967-0661(95)00192-w

✦ Synopsis

The extended Kalman filter (EKF) is formulated as a parameter estimator and used to estimate position sensor bias and actuator current bias signals for the industrial actuator benchmark system. These bias estimates are compared instantaneously to a threshold for fault detection and identification (FDI). The paper reports results for applying this method to given benchmark data. The FDI performance is good for detecting position sensor and actuator current faults in the presence of unmodeled nonlinear dynamics and an unmodeled load change for small-amplitude signal conditions when the EKF implementation assumes parameter pseudonoise and a slow decay in the parameter dynamics. For large-amplitude signals, the results are reasonably good, but they suggest that a more accurate model for a saturation nonlinearity could improve the method's FDI performance.