Design and Performance Evaluation of Residual Generators for the FDI of an Aircraft

In this work, several procedures for the fault detection and isolation （FDI） on general aviation aircraft sensors are presented. In order to provide a comprehensive wide-pectrum treatment, both linear and nonlinear, model-based and data-driven methodologies are considered. The main contributions of the paper are related to the development of both FDI polynomial method （PM） and FDI scheme based on the nonLinear geometric approach （NLGA）. As to the PM, the obtained results highlight a good trade-off between solution complexity and resulting performances. Moreover, the proposed PM is especially useful when robust solutions are required for minimising the effects of modelling errors and noise, while maximising fault sensitivity. As to the NLGA, the proposed work is the first development and robust application of the NLGA to an aircraft model in flight conditions characterised by tight-oupled longitudinal and lateral dynamics. In order to verify the robustness of the residual generators related to the previous FDI techniques, the simulation results adopt a typical aircraft reference trajectory embedding several steady-tate flight conditions, such as straight flight phases and coordinated turns. Moreover, the simulations are performed in the presence of both measurement and modelling errors. Finally, extensive simulations are used for assessing the overall capabilities of the developed FDI schemes and a comparison with neural networks （NN） and unknown input Kalman filter （UIKF） diagnosis methods is performed.

Numerical study of the influence of the atmospheric pressure on the thermal environment in the passenger cabin

The cabin air pressure remains lower than the horizontal atmospheric pressure when the airplane is in flight.Air pressure is one of the parameters that must be taken into consideration while studying the thermal environment of an airplane cabin.There are still no reference values for aircraft cabins despite the fact that numerous studies on low pressure heat transfer have demonstrated the connection between convective heat transfer coefficient(CHTC)and air pressure.In this paper,a correction method for CHTC under low pressure conditions was established by using the dummy heat dissipation in the low-pressure cabin experiment.On this basis,a thermal environment simulation model was developed,then was applied to the simulation of a seven-row aircraft cabin containing 42 passengers,and the CHTC and heat loss of dummy surface in the cabin were obtained.Finally,the results of PMV calculated by using heat dissipation and air parameters at sampling points were compared.The results show that the modified CHTC can accurately reflect the cabin thermal environment under low pressure conditions,and the correction of CHTC can be realized by adjusting the turbulent Prandtl number,which is nonlinear correlated with the pressure.The simulation results of the thermal environment in the seven-row cabin show that the CHTC changes by about 42%before and after modification.The air pressure decreases during take-off,which reduces the average CHTC of the crew surface from 5.09 W/(m^(2)·K)to 4.56 W/(m^(2)·K),but the air temperature rises by about 0.2°C as a whole.The deviation of PMV results calculated by using simulated heat loss data and using air parameters of measuring points in space is up to 0.5,but the latter is representative for calculating the thermal comfort level of the whole cabin.

A new vortex sheet model for simulating aircraft wake vortex evolution

A new vortex sheet model was proposed for simulating aircraft wake vortex evolution.Rather than beginning with a pair of counter-rotating cylindrical vortices as in the traditional models, a lift-drag method is used to initialize a vortex sheet so that the roll-up phase is taken into account. The results of this model report a better approximation to a real situation when compared to the measurement data. The roll-up induced structures are proved to influence the far-field decay.On one hand, they lead to an early decay in the diffusion phase. On the other hand, the growth of linear instability such as elliptical instability is suppressed, resulting in a slower decay in the rapid decay phase. This work provides a simple and practicable model for simulating wake vortex evolution, which combines the roll-up process and the far-field phase in simulation. It is also proved that the roll-up phase should not be ignored when simulating the far-field evolution of an aircraft wake vortex pair, which indicates the necessity of this new model.

Decoding pilot behavior consciousness of EEG, ECG, eye movements via an SVM machine learning model

To decode the pilot’s behavioral awareness,an experiment is designed to use an aircraft simulator obtaining the pilot’s physiological behavior data.Existing pilot behavior studies such as behavior modeling methods based on domain experts and behavior modeling methods based on knowledge discovery do not proceed from the characteristics of the pilots themselves.The experiment starts directly from the multimodal physiological characteristics to explore pilots’behavior.Electroencephalography,electrocardiogram,and eye movement were recorded simultaneously.Extracted multimodal features of ground missions,air missions,and cruise mission were trained to generate support vector machine behavior model based on supervised learning.The results showed that different behaviors affects different multiple rhythm features,which are power spectra of theθwaves of EEG,standard deviation of normal to normal,root mean square of standard deviation and average gaze duration.The different physiological characteristics of the pilots could also be distinguished using an SVM model.Therefore,the multimodal physiological data can contribute to future research on the behavior activities of pilots.The result can be used to design and improve pilot training programs and automation interfaces.

Use of multimodal physiological signals to explore pilots’cognitive behaviour during flight strike task performance

This study explored the use of multi-physiological signals and simultaneously recorded high-density electroencephalography(EEG),electrocardiogram(ECG),and eye movements to better understand pilots’cognitive behaviour during flight simulator manoeuvres.Multimodal physiological signals were collected from 12 experienced pilots with international aviation qualifications under the wide-angle and impressive vision simulation.The data collection spanned two flight strike missions,each with three mission intensities,resulting in a data set of EEG,ECG,and eye movement signals from six subtasks.The multimodal data were analysed using signal processing methods.The results indicated that,when the flight missions were performed,the pilots’physiological characteristics exhibited rhythmic changes in the power spectrum ofθwaves in the EEG,r-MSSD in the ECG,and average gaze duration.Furthermore,the pilots’physiological signals were more sensitive during the target mission than during the empty target mission.The results also showed correlations between different physiological characteristics.We showed that specific multimodal features are useful for advancing neuroscience research into pilots’cognitive behaviour and processes related to brain activity,psychological rhythms,and eye movement.