| Affiliation: | 1. College of IOT Engineering, Hohai University, Changzhou 213022, China;2. College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China;1. D.E.I.M. (Department of Energy Information Engineering and Mathematical Models) of the National Research Council (CNR) of Italy, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy;2. The School of Engineering and Physics, The University of the South Pacific, Laucala Campus, Suva, Fiji Islands;3. I.S.S.I.A. C.N.R. Section of Palermo (Institute on Intelligent Systems for Automation), via Dante 12, Palermo 90128, Italy;1. Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza Leonardo da Vinci 32, 20133 Milano, Italy;2. Politecnico di Milano, Dipartimento di Ingegneria Civile e Ambientale, Piazza Leonardo da Vinci 32, 20133 Milano, Italy |
| Abstract: | The existing multiple model-based estimation algorithms for Fault Detection and Diagnosis (FDD) require the design of a model set, which contains a number of models matching different fault scenarios. To cope with partial faults or simultaneous faults, the model set can be even larger. A large model set makes the computational load intensive and can lead to performance deterioration of the algorithms. In this paper, a novel Double-Model Adaptive Estimation (DMAE) approach for output FDD is proposed, which reduces the number of models to only two, even for the FDD of partial and simultaneous output faults. Two Selective-Reinitialization (SR) algorithms are proposed which can both guarantee the FDD performance of the DMAE. The performance is tested using a simulated aircraft model with the objective of Air Data Sensors (ADS) FDD. Another contribution is that the ADS FDD using real flight data is addressed. Issues related to the FDD using real flight test data are identified. The proposed approaches are validated using real flight data of the Cessna Citation II aircraft, which verified their effectiveness in practice. |
