Double-model adaptive fault detection and diagnosis applied to real flight data. (March 2015) - Lu, Peng
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. Abstract : Author-Highlights: Two novel SR algorithms are proposed to enhance the performance of the DMAE. The DMAE approaches can reduce the computational load of the MMAE approaches. The DMAE can detect, isolate and estimate consecutive and simultaneous faults. The influence of model uncertainties is reduced by using the kinematic equations. The FDIE of the DMAE is validated using both simulated and real flight test data.