Air Data Sensors Research Articles

Flight Testing Of A Single Point Frequency Sweeping Filtered Rayleigh Scattering Probe

"We present a successful in-flight application of filtered Rayleigh scattering (FRS) for temperature measurement in close proximity to the air plane using a newly developed single point, frequency sweeping probe. The temperature measurement is of relevance in the quantification of the density required for the evaluation of velocity from Pitot-probe measurements to obtain the true air speed (TAS). Laser optical air data sensors have the potential for contactless measurements outside the aerodynamic boundary layer. They are capable of self-diagnosis of fault conditions (e.g. signal loss, increased measurement uncertainty or spectral distortion for FRS) thereby increasing the safety of the aircraft. Compared to classical probes, they are less susceptible potential icing and blockage issues. The FRS-results presented here, are the outcome of two separate flight campaigns, carried out in the Alpine region as well as in the North Sea area. Measurement data provided information on the functionality of the FRS-system under various flight and weather conditions. Furthermore, the implementation of the FRS measurement system in the aircraft is presented; application aspects and improvements are discussed. In particular, the measurement accuracy at the actually achievable measurement rate was analyzed. For straight ahead flights at constant height, temperature mean values were in good agreement with the flight data system of the DLR research aircraft Dassault Falcon 20 (D-CMET). Aiming at in-flight air data measurements, the optical FRS measurement system requires a real-time temperature sample rate of a few Hz. We demonstrate that 2 FRS spectra per second can be acquired by a newly developed method using frequency sweeps for temperature determination. The low signal intensity of cw-laser based FRS is countered using a combined transmitter/receiver optic that focusses the entire cw-laser power into one measuring point. The light backscattered from the probe volume is collected by the same optics, filtered and imaged in a confocal arrangement onto a photomultiplier. An improved and miniaturized version of the presented FRS system could be a part of a future optical air data system (OADS). However, the strength of FRS signal relative to bright ambient conditions (e.g. sunlight, cloud backscatter) must be further improved in order to be able to measure well above cloud cover. The potential for further development is foreseen even if the accuracy required for flight data systems (~0.5 K) has not yet been achieved at a temperature measurement rate of 2 Hz. "

Practical examples for the flight data compatibility check

Accurate information about aircraft speed, altitude, and aerodynamic flow angles is essential for evaluating aircraft performance and handling qualities. These quantities are determined from air data measurements taken by sensors normally located near the aircraft cockpit. Since these sensors are affected by the distorted flow field around the fuselage, a correction must be applied. Before the first flight, a set of calibration parameters is usually determined from wind tunnel experiments or CFD calculations. However, the Data Compatibility Check (DCC) method allows a more accurate air data sensor calibration during the certification flight test. This method reconstructs air data quantities from inertial acceleration, angular rate measurements and the flight path. By comparing the reconstructed quantities with the measured ones, the structure and parameters of air data sensor models can be identified. In this paper, an introduction to the data compatibility check method and the setup used in a flight test for system identification is given. The DCC is applied on data gathered from a test campaign with the new DLR research aircraft Dassault Falcon 2000LX ISTAR. Use cases for the calibration of the nose boom airflow vanes and the correction of sensors during large sideslip maneuvers will be presented in this paper.

Cfd Tools as Applied to Indian Combat Aircraft Configuration-an Experience

Computational Fluid Dynamics (CFD) tools are extensively used by aircraft industries all over the world in design and development of airplanes, with emphasis on reducing both the cost and design cycle time. This paper deals with the use of CFD tools at Aeronautical Development Agency (ADA) in design and development of Indian combat aircraft. The applications include: (1) evaluation of incremental changes in aerodynamic configuration, (2) wave drag evaluation, (3) aerodynamic loads, (4) flow field around air data sensor locations, (5) Euler calculation for coupled internal and external flow field, and (6) viscous computation of high angles of attack flows. In the initial design phase, codes based on the potential theory were used for wing design and fuselage shape optimization. Later indigenously developed Euler and Navier-Stokes codes were employed for the various applications. This paper brings out the strength and weakness of CFD as experienced at ADA and its comparison with the Wind Tunnel and Flight Data.

Real-time estimation of altitude and airspeed under large airflow angles in flight test

In the flight test, many test subjects need to enter the large airflow angle. Due to the large airflow angle, the position error of air data sensor is big, and the altitude and airspeed of the aircraft are not accurate, which not only affects the effectiveness of the test results, but also affects the safety of the test flight. This paper proposes two real-time altitude and airspeed estimation methods, including the GPS method and constant wind field method. The GPS method is commonly used in the airspeed calibration, which mainly uses the GPS height change to correct the static pressure, but it depends on the aircraft total pressure sensor to get the airspeed. The constant wind field method directly calculates the true airspeed not depending on the pressure measurement, but it is greatly affected by the wind stability. During the flight test, using this method, the real-time monitoring can provide pilots with accurate altitude and airspeed reference.

Recent Applications of System Identification Techniques at IPEV

This paper presents an overview of the system identification techniques developed and applied at the Flight Test and Research Institute during the last decade. System identification techniques have been used extensively to obtain aerodynamic models for a flight-test simulator, which was developed in house and specifically tailored for flight-test instruction and preparation for actual test flights. The methods applied were mostly the output error for data compatibility check and equation error in the time domain for model structure selection and parameter estimation. The most relevant applications are summarized in this paper, including identification of a high angle of attack model with compressibility effects for a jet trainer; estimation of drag polars at high subsonic regime for a jet fighter; identification of aerodynamic model in real-time for a propeller-driven fighter; application of flight-path reconstruction techniques to spin tests, in order to correct errors in the air data sensors; and identification of power effects on the longitudinal stability of a propeller-driven trainer. The examples show that system identification has become crucial to 1) obtain accurate aerodynamic models for training in the flight-test simulator and 2) develop more efficient flight-test techniques.

Robust fault detection and diagnosis of primary air data sensors in the presence of atmospheric turbulence

Abstract This paper presents a fault detection and diagnosis (FDD) algorithm for various faults in the primary air data sensors (PADS) of an aircraft in the presence of external disturbances such as atmospheric turbulence. Rapid wind variations due to turbulence induce excessive error in the externally fitted air data probe measurements, which may lead to loss of control and misinterpretations by the flight crew. In adverse environmental conditions, the FDD of air data prefers robust and adaptive air data estimates that use an analytical redundancy approach with fewer computations. The proposed method considers the kinematics of the aircraft instead of the dynamics used in the state-of-the-art algorithms. The advantage of using kinematics is that it can reduce modeling errors significantly, avoiding high false alarm rates in the FDD process. For the estimation of stable and accurate air data under external disturbance, the inertial navigation system and global positioning system (INS/GPS) output are considered instead of actual air data probe or sensor measurements. The proposed algorithm uses estimates of air data using an exponentially weighted adaptive extended Kalman filter (EW-AEKF) to detect and diagnose PADS faults, which can perform well even in the presence of uncertain noise due to atmospheric turbulence experienced during flight. The simulation was carried out to validate the algorithm with flight data obtained from the X-Plane flight simulator under moderate atmospheric turbulence. The simulation experiments were carried out using the MATLAB programming platform. The results show that the proposed method achieves satisfactory FDD performance with lower root mean square error (RMSE) and computation time than traditional EKF-based algorithms.

Robust Data-Driven Fault Detection: An Application to Aircraft Air Data Sensors

Fault detection (FD) is important for health monitoring and safe operation of dynamical systems. Previous studies use model-based approaches which are sensitive to system specifics, attenuating the robustness. Data-driven methods have claimed accurate performances which scale well to different cases, but the algorithmic structures and enclosed operations are “black,” jeopardizing its robustness. To address these issues, exemplifying the FD problem of aircraft air data sensors, we explore to develop a robust (accurate, scalable, explainable, and interpretable) FD scheme using a typical data-driven method, i.e., deep neural networks (DNN). To guarantee the scalability, aircraft inertial reference unit measurements are adopted as equivalent inputs to the DNN, and a database associated with 6 different aircraft/flight conditions is constructed. Convolutional neural networks (CNN) and long-short time memory (LSTM) blocks are used in the DNN scheme for accurate FD performances. To enhance robustness of the DNN, we also develop two new concepts: “large structure” which corresponds to the parameters that can be objectively optimized (e.g., CNN kernel size) via certain metrics (e.g., accuracy) and “small structure” that conveys subjective understanding of humans (e.g., class activation mapping in CNN) within a certain context (e.g., object detection). We illustrate the optimization process we adopted in devising the DNN large structure, which yields accurate (90%) and scalable (24 diverse cases) performances. We also interpret the DNN small structure via class activation mapping, which yields promising results and solidifies the robustness of DNN. Lessons and experiences we learned are also summarized in the paper, which we believe is instructive for addressing the FD problems in other similar fields.

Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy With a Temporal Convolutional Network

A synthetic air data system (SADS) is an analytical redundancy technique that is crucial for unmanned aerial vehicles (UAVs) and is used as a backup system during air data sensor failures. Unfortunately, the existing state-of-the-art approaches for SADS require GPS signals or high-fidelity dynamic UAV models. To address this problem, a novel synthetic airspeed estimation method that leverages deep learning and an unscented Kalman filter (UKF) for analytical redundancy is proposed. Our novel fusion-based method only requires an inertial measurement unit (IMU), elevator control input, and airflow angles while GPS, lift/drag coefficients, and complex aircraft dynamic models are not required. Additionally, we demonstrate that our proposed temporal convolutional network (TCN) is a more efficient model for airspeed estimation than the renowned models, such as ResNet or bidirectional long short-term memory (LSTM). Our deep learning-aided UKF was experimentally verified on long-duration real flight data and has promising performance compared with the state-of-the-art methods. In particular, it is confirmed that our proposed method robustly estimates the airspeed even under dynamic flight conditions where the performance of conventional methods is degraded.

The Fly-by-Light system for military aircraft

This paper presents the authors’ recent documentary research on the benefits of using the Fly-by-Light system in Military aircraft and the understanding of why Fly-by-Light (FBL) is very promising for the further development of military aircraft. The Fly-by-Light flight system is characterized by the fact that the input control signals from the pilot, motion data, and air data sensors are sent to the flight control computer and from there to the actuators of the control surfaces through a fiber optic cable and the feedback from actuators sensors or other signals is received in the same way, through fiber optic cable connected to the aircraft flight control computer. The first major step in the evolution of flight control systems was the replacement of mechanical flight control systems with a Fly-by-Wire (FBW) flight control system. The next major step is the replacement of Fly-by-Wire (FBW) flight control system with a Fly-by-Light (FBL) flight control system which has some significant advantages such as: reducing the weight of the system, increasing the speed and volume of information transmitted through fiber-optic cable (input and feedback signals), keeping intact the accuracy of the signals along the entire length of the circuit; also, the optical fibers do not present any risk of fire, as they carry only light signals, and the temperature, humidity, and severe weather conditions do not affect the fiber optic cable, which can withstand a pressure of about 0.69 - 1.38 MPa without damaging the cable, unaffected by Electromagnetic Interference (EMI) and the Electromagnetic Pulse (EMP) generated by nuclear blasts. All the advantages listed above provide tactical and safety advantages for the military aircraft and its crew.

A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation.

Recent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques using flight data. Linear regression models, identified from data, are derived to build primary and transformed residuals. These residuals are then implemented to develop fault isolation schemes for 14 sensors of a semi-autonomous aircraft. Specifically, directional Mahalanobis distance-based and fault reconstruction-based techniques are compared in terms of their FI and FE performance. Then, a bank of Bayesian filters is proposed to compute, in flight, the fault belief for each sensor. Both the training and the validation of the schemes are performed using data from multiple flights. Artificial faults are injected into the fault-free sensor measurements to reproduce the occurrence of failures. A detailed evaluation of the techniques in terms of FI and FE performance is presented for failures on the air-data sensors, with special emphasis on the True Air Speed (TAS), Angle of Attack (AoA), and Angle of Sideslip (AoS) sensors.

Design and Integration of a Dual Redundancy Air Data System for Unmanned Air Vehicles

Air data systems measure airspeed, pressure altitude, angle of attack and angle of sideslip. These measurements are essential for operating flight control laws to ensure safe flights. Since the loss or corruption of air data measurements is considered as catastrophic, a high level of operational reliability needs to be achieved for air data systems. In the case of unmanned air vehicles, failure of any of air data sensors is more critical due to the absence of onboard pilot decision aid. This paper presents design of a dual redundancy air data system and the integration process for an unmanned air vehicle. The proposed dual-redundant architecture is based on two independent air data probes and redundancy management by central processing in two independent flight control computers. Starting from unit testing of single air data sensor, details are provided of system level tests used to meet overall requirements. Test results from system integration demonstrate the efficiency of the proposed process. Keywords: ëŒ€ê¸°ìžë£Œì‹œìŠ¤í œ, 이중화설계, 체계통합, ëŒ€ê¸°ìžë£Œë³´ì • Keywords: Air Data System, Dual Redundancy Design, System Integration, Air Data Calibration

Air Data Sensor Fault Detection and Diagnosis in the Presence of Atmospheric Turbulence: Theory and Experimental Validation With Real Flight Data

Managing air data sensor fault detection and diagnosis (FDD) in the presence of atmospheric turbulence is challenging since the effects of faults and turbulence are coupled. Existing FDD approaches cannot decouple the faults from the turbulence. To address this challenge, this brief first proposes a novel kinematic model that incorporates the effects of the turbulence. This model is valid inside the entire flight envelope, and there is no need to design a linear parameter varying system. Then, the double-model adaptive estimation algorithm is extended to achieve unbiased state estimation even in the presence of unknown disturbances. The proposed approach is validated using generated turbulence data with various scale lengths and intensities. More importantly, the proposed approach is successfully validated using the real flight test data of a business jet when it is experiencing atmospheric turbulence.

An innovative analytic redundancy approach to air data sensor fault detection

ABSTRACTThis article presents a potential analytic redundancy approach to detect faults in the air data sensor of an aircraft. In modern aircraft, fault detection of air data sensors is performed using a complex voting mechanism, which requires the availability of redundant air data sensor in all situations. However, to continuously monitor operation and performance of these sensors, the analytic redundancy-based air data estimation and fault detection is highly preferred than estimation with air data probe measurements. The proposed algorithm uses the kinematics of aircraft to estimate air data and detect air data sensor fault. In this paper, a simple mathematical model is developed, which does not consider the forces and moments acting on aircraft and uses measurements only from the Inertial Measurement Unit (IMU) and Navigation System Data (NSD). In order to implement this approach, the Iterated Optimal Extended Kalman Filter (IOEKF) is developed to estimate air data, which provides an accurate and stable estimation. With the estimated states, the physical air data sensor measurements are compared and the residual is calculated to track each sensor performance and to detect the occurrence of a fault. The key advantage of this approach is that it does not require complex dynamic equations and is free from system uncertainties. The proposed algorithm is simulated in MATLAB software using flight simulator flight data and validated using the real-time flight data of Cessna Citation II transport aircraft.

Fuzzy-adaptive constrained data fusion algorithm for indirect centralized integrated SINS/GNSS navigation system

The main challenge of low-cost strap-down inertial navigation systems (SINSs) is time-growing positioning error due to erroneous measurements of micro-electro mechanical system (MEMS)-based inertial sensors. The global navigation satellite system (GNSS) provides drift-free positioning data that can be appropriately utilized to prevent the cumulative error of stand-alone SINS. The primary aim of this research is to enhance the positioning accuracy, performance, and reliability of low-cost SINS/GNSS-integrated navigation system. To attain this, we propose an applied data fusion algorithm for indirect centralized (IC) integrated SINS/GNSS. The proposed data fusion algorithm is based on fuzzy-adaptive constrained estimation filter. Velocity and altitude constraints are embedded in the integration scheme of the proposed SINS/GNSS system to preserve system reliability during abrupt GNSS outage. In an innovative way, the state constraints of altitude are defined based on the measurements of air-data sensors. The state estimation is effectively optimized since the respective states are projected on a constraint surface. Furthermore, a fuzzy type-2 inference system is developed for adaptively changing the covariance matrix of the estimation algorithm. Inertial measurements are used as the input of the fuzzy inference system. Accordingly, the state estimation algorithm is adaptively modified based on the vehicle’s maneuvering during the navigation trajectory. The proposed SINS/GNSS system is experimentally assessed through several vehicular tests. The results indicate that not only does the proposed algorithm improve the navigation accuracy, it will also enhance the reliability of the integrated navigation system during GNSS outage.

Performance of Low-Cost Air-Data Sensors for Airspeed and Angle of Attack Measurements in a Flapping-Wing Robot

AbstractAir-data measurements are one of the essential observations for system identification of flapping-wing unmanned aerial vehicles (UAVs). The effects of intensive high-amplitude oscillations ...

Limitations of Flight Path Reconstruction techniques

The Flight Path Reconstruction (FPR) techniques are performed to verify the data compatibility check and post-flight. This is often achieved by calibrating the onboard sensors such as inertial and airdata sensors. In this paper, the limitations of FPR techniques in terms of Maximum Likelihood Estimation (MLE) and Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) have been reported. To demonstrate the FPR and sensor calibration, kinematic trajectory simulations with wind box type maneuvers have been performed. It is also shown as how a kinematic simulation is valid for the studies carried out in this work.

Air Data Sensor Fault Detection with an Augmented Floating Limiter

Although very uncommon, the sequential failures of all aircraft Pitot tubes, with the consequent loss of signals for all the dynamic parameters from the Air Data System, have been found to be the cause of a number of catastrophic accidents in aviation history. This paper proposes a robust data-driven method to detect faulty measurements of aircraft airspeed, angle of attack, and angle of sideslip. This approach first consists in the appropriate selection of suitable sets of model regressors to be used as inputs of neural network-based estimators to be used online for failure detection. The setup of the proposed fault detection method is based on the statistical analysis of the residual signals in fault-free conditions, which, in turn, allows the tuning of a pair of floating limiter detectors that act as time-varying fault detection thresholds with the objective of reducing both the false alarm rate and the detection delay. The proposed approach has been validated using real flight data by injecting artificial ramp and hard failures on the above sensors. The results confirm the capabilities of the proposed scheme showing accurate detection with a desirable low level of false alarm when compared with an equivalent scheme with conventional “a priori set” fixed detection thresholds. The achieved performance improvement consists mainly in a substantial reduction of the detection time while keeping desirable low false alarm rates.

Experimental interval models for the robust Fault Detection of Aircraft Air Data Sensors

In this paper data-based approaches for a robust Fault Detection (FD) of the Air Data Sensors (ADS) including airspeed angles of attack and sideslip are proposed. Experimental Interval Models (IMs) have been considered for coping with modeling uncertainty and for providing interval estimations of the ADS signals. Specifically, a nonlinear-in-the-parameter Neural Network model has been introduced to characterize the nominal nonlinear response in the different phase of the flight, while model uncertainty is captured by an additional additive contribution provided by a linear in the parameters IM. The FD is achieved by verifying whether the measured ADS signals fall within time-varying bounds predicted by the nonlinear + IM. The IM identification has been formalized as a Linear Matrix Inequality (LMI) problem using as cost function the mean amplitude of the prediction interval and, as optimization variables, the amplitudes of the uncertain parameters of the model. The model identification was based on multi flight experimental data of a P92 Tecnam aircraft. The proposed method is compared with conventional FD schemes with fixed thresholds. Extensive validation tests have been conducted by injecting artificially additive hard and incipient failures on the ADS. The FD scheme has shown to be remarkably robust in all phases of the flight in terms of low false alarm rates while maintaining desirable detectability to faults.

Air Data Estimation by Fusing Navigation System and Flight Control System

A novel synthetic air data estimation method without using air data sensors is presented, and the method only relies on the information from the Navigation System (NS) and Flight Control System (FCS). The aircraft's aerodynamic model is also required to make a connection between the FCS control parameters and the NS measurements. The airspeed, angle of attack and sideslip, angular velocity and wind speed are defined as state vectors, and state equations are established through the aircraft's aerodynamic model and dynamics. Linear velocity and angular velocity provided by the navigation system are considered as the measurement vector. To deal with variable wind fields, a novel Initialised Three-step Extended Kalman Filter (ITEKF), which considers the wind speed as an unknown input, is developed to track the variation of wind speed. Simulation results based on a Generic Hypersonic Vehicle (GHV) model are presented and compared with an existing method. Factors affecting the method's accuracy include the navigation system accuracy and the aerodynamic model error, are also discussed.

Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach.

Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system.