Double-model adaptive fault detection and diagnosis applied to real flight data. (March 2015)

• Record Type: Journal Article
• Authors: Lu, Peng; Van Eykeren, Laurens; van Kampen, Erik-Jan; de Visser, Coen; Chu, Qiping
• Is Part Of: Control engineering practice. Volume 36(2015)
• Page Start: 39
• Page End: 57
• Publication Date: 2015-03
• Subjects: Fault detection and diagnosis -- Air data sensors -- Double-model adaptive estimation -- Real flight test data -- Unscented Kalman filter; ADDSAFE advanced fault diagnosis for sustainable flight guidance and control -- ADS air data sensors -- DMAE double-model adaptive estimation -- DMAE-NSR double-model adaptive estimation-no selective reinitialization -- FDD fault detection and diagnosis -- FDI fault detection and isolation -- GPS global positioning systems -- IMM interacting multiple-model -- IMU inertial measurement unit -- MM multiple-model -- MMAE multiple-model adaptive estimation -- SRMMAE selective-reinitialization multiple-model adaptive estimation -- SR selective-reinitialization -- UKF unscented Kalman filter; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2014.12.007
• Languages: English
• ISSNs: 0967-0661

Abstract

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.