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Sensor and Actuator Fault Detection, Isolation and Recovery for Autonomous and Transport Vehicles

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 15991

Special Issue Editors


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Guest Editor
School of Future Transport Engineering, Faculty of Engineering, Environment and Computing, Coventry University, Coventry CV1 5FB, UK
Interests: fault tolerance; control; motion estimation; system identification; aircraft; spacecraft
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
ONERA (Office National d’Etudes et de Recherches Aerospatiales), 91120 Palaiseau, France
Interests: particle filtering; aircraft navigation; fault tolerance; system identification; Kalman filtering

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Guest Editor
Department of Industrial Engineering, Bologna University, 40126 Bologna BO, Italy
Interests: flight mechanics; control, UAV; spacecraft; autonomous aircraft; recovery from actuator failures
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute for Future Transport and Cities, School of Mechanical, Aerospace and Automotive Engineering, Faculty of Engineering, Environment and Computing, Coventry University, Coventry CV1 5FB, UK
Interests: autonomous systems; artificial intelligence; avionics systems; safety-critical systems; renewable energy

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Guest Editor
Institute for Future Transport and Cities, School of Mechanical, Aerospace and Automotive Engineering, Faculty of Engineering, Environment and Computing, Coventry University, Coventry CV1 5FB, UK
Interests: artificial intelligence; autonomy; UAVs; UAV swarming

Special Issue Information

Dear Colleagues,

Fault tolerance has gained importance in recent years given the trends towards higher levels of autonomy, particularly in aircraft guidance, navigation and control, but also for other ground and air transport vehicles, unmanned aircraft, aerospace or spacecraft systems. Increasingly, autonomous aircraft rely more heavily on sensing and actuation that can become faulty. A key to increasing functional safety and autonomy in different types of vehicles including aircraft is the development of sensor and actuator fault detection, isolation and recovery methods. The methods used range from statistical tests on residuals for fault detection, linear and nonlinear state and parameter estimation for fault diagnosis and estimation and control reconfiguration to recover from faults, as well as artificial intelligence based methods. The challenges in developing such systems range from the ability to detect and distinguish between fault modes, robustness to uncertainty, the ability to detect false alarms and recover from faults quickly and accurately to failure modes effects and analysis. Therefore, this Special Issue will bring together papers which particularly describe recent advances fault detection, isolation and recovery for ground and air transport vehicles, unmanned aircraft, aerospace or spacecraft systems, including but not limited to aircraft navigation and control systems. Papers with theoretical, simulation and practical experimental results in this field are all encouraged.

 

Dr. Nadjim Horri
Dr. Karim Dahia
Dr. Fabrizio Giulietti
Dr. Thomas Statheros
Dr. Mauro Innocente
Guest Editors

Manuscript Submission Information

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Keywords

  • aircraft
  • sensor
  • fault detection
  • isolation
  • recovery
  • actuator
  • control

Published Papers (8 papers)

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Research

23 pages, 10019 KiB  
Article
Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion
by Wenkui Hou, Wanyu Li and Pengyu Li
Sensors 2023, 23(11), 5110; https://doi.org/10.3390/s23115110 - 26 May 2023
Cited by 6 | Viewed by 1699
Abstract
The reliability of autonomous driving sensing systems impacts the overall safety of the driving system. However, perception system fault diagnosis is currently a weak area of research, with limited attention and solutions. In this paper, we present an information-fusion-based fault-diagnosis method for autonomous [...] Read more.
The reliability of autonomous driving sensing systems impacts the overall safety of the driving system. However, perception system fault diagnosis is currently a weak area of research, with limited attention and solutions. In this paper, we present an information-fusion-based fault-diagnosis method for autonomous driving perception systems. To begin, we built an autonomous driving simulation scenario using PreScan software, which collects information from a single millimeter wave (MMW) radar and a single camera sensor. The photos are then identified and labeled via the convolutional neural network (CNN). Then, we fused the sensory inputs from a single MMW radar sensor and a single camera sensor in space and time and mapped the MMW radar points onto the camera image to obtain the region of interest (ROI). Lastly, we developed a method to use information from a single MMW radar to aid in diagnosing defects in a single camera sensor. As the simulation results show, for missing row/column pixel failure, the deviation typically falls between 34.11% and 99.84%, with a response time of 0.02 s to 1.6 s; for pixel shift faults, the deviation range is between 0.32% and 9.92%, with a response time of 0 s to 0.16 s; for target color loss, faults have a deviation range of 0.26% to 2.88% and a response time of 0 s to 0.05 s. These results prove the technology is effective in detecting sensor faults and issuing real-time fault alerts, providing a basis for designing and developing simpler and more user-friendly autonomous driving systems. Furthermore, this method illustrates the principles and methods of information fusion between camera and MMW radar sensors, establishing the foundation for creating more complicated autonomous driving systems. Full article
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31 pages, 11911 KiB  
Article
Observer-Based Optimal Control of a Quadplane with Active Wind Disturbance and Actuator Fault Rejection
by Zaidan Zyadat, Nadjim Horri, Mauro Innocente and Thomas Statheros
Sensors 2023, 23(4), 1954; https://doi.org/10.3390/s23041954 - 09 Feb 2023
Cited by 2 | Viewed by 2356
Abstract
Hybrid aircraft configurations with combined cruise and vertical flight capabilities are increasingly being considered for unmanned aircraft and urban air mobility missions. To ensure the safety and autonomy of such missions, control challenges including fault tolerance and windy conditions must be addressed. This [...] Read more.
Hybrid aircraft configurations with combined cruise and vertical flight capabilities are increasingly being considered for unmanned aircraft and urban air mobility missions. To ensure the safety and autonomy of such missions, control challenges including fault tolerance and windy conditions must be addressed. This paper presents an observer-based optimal control approach for the active combined fault and wind disturbance rejection, with application to a quadplane unmanned aerial vehicle. The quadplane model is linearised for the longitudinal plane, vertical takeoff and landing and transition modes. Wind gusts are modelled using a Dryden turbulence model. An unknown input observer is first developed for the estimation of wind disturbance by defining an auxiliary variable that emulates body referenced accelerations. The approach is then extended to simultaneous rejection of intermittent elevator faults and wind disturbance velocities. Estimation error is mathematically proven to converge to zero, assuming a piecewise constant disturbance. A numerical simulation analysis demonstrates that for a typical quadplane flight profile at 100 m altitude, the observer-based wind gust and fault correction significantly enhances trajectory tracking accuracy compared to a linear quadratic regulator and to a H-infinity controller, which are both taken, without loss of generality, as benchmark controllers to be enhanced. This is done by adding wind and fault compensation terms to the controller with admissible control effort. The proposed observer is also shown to enhance accuracy and observer-based rejection of disturbances and faults compared to three alternative observers, based on output error integration, acceleration feedback and a sliding mode observer, respectively. The proposed approach is particularly efficient for the active rejection of actuator faults under windy conditions. Full article
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29 pages, 1711 KiB  
Article
Model-Based Condition Monitoring of the Sensors and Actuators of an Electric and Automated Vehicle
by Shiqing Li, Michael Frey and Frank Gauterin
Sensors 2023, 23(2), 887; https://doi.org/10.3390/s23020887 - 12 Jan 2023
Cited by 7 | Viewed by 1799
Abstract
Constant monitoring of driving conditions and observation of the surrounding area are essential for achieving reliable, high-quality autonomous driving. This requires more reliable sensors and actuators, as there is always the potential that sensors and actuators will fail under real-world conditions. The sensitive [...] Read more.
Constant monitoring of driving conditions and observation of the surrounding area are essential for achieving reliable, high-quality autonomous driving. This requires more reliable sensors and actuators, as there is always the potential that sensors and actuators will fail under real-world conditions. The sensitive condition-monitoring methods of sensors and actuators should be used to improve the reliability of the sensors and actuators. They should be able to detect and isolate the abnormal situations of faulty sensors and actuators. In this paper, a developed model-based method for condition monitoring of the sensors and actuators in an electric vehicle is presented that can determine whether a sensor has a fault and further reconfigure the sensor signal, as well as detect the abnormal behavior of the actuators with the reconfigured sensor signals. Through the simulation data obtained by the vehicle model in complex road conditions, it is proved that the method is effective for the state detection of sensors and actuators. Full article
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18 pages, 2209 KiB  
Article
An Interleaved Segmented Spectrum Analysis: A Measurement Technique for System Frequency Response and Fault Detection
by Alejandro Roman-Loera, Anurag Veerabathini, Jorge E. Macias-Diaz and Felipe de Jesus Rizo-Diaz
Sensors 2022, 22(18), 6757; https://doi.org/10.3390/s22186757 - 07 Sep 2022
Cited by 2 | Viewed by 1531
Abstract
A frequency spectrum segmentation methodology is proposed to extract the frequency response of circuits and systems with high resolution and low distortion over a wide frequency range. A high resolution is achieved by implementing a modified Dirichlet function (MDF) configured for multi-tone excitation [...] Read more.
A frequency spectrum segmentation methodology is proposed to extract the frequency response of circuits and systems with high resolution and low distortion over a wide frequency range. A high resolution is achieved by implementing a modified Dirichlet function (MDF) configured for multi-tone excitation signals. Low distortion is attained by limiting or avoiding spectral leakage and interference into the frequency spectrum of interest. The use of a window function allowed for further reduction in distortion by suppressing system-induced oscillations that can cause severe interference while acquiring signals. This proposed segmentation methodology with the MDF generates an interleaved frequency spectrum segment that can be used to measure the frequency response of the system and can be represented in a Bode and Nyquist plot. The ability to simulate and measure the frequency response of the circuit and system without expensive network analyzers provides good stability coverage for reliable fault detection and failure avoidance. The proposed methodology is validated with both simulation and hardware. Full article
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21 pages, 2690 KiB  
Article
Failure Detection in Quadcopter UAVs Using K-Means Clustering
by James Cabahug and Hossein Eslamiat
Sensors 2022, 22(16), 6037; https://doi.org/10.3390/s22166037 - 12 Aug 2022
Cited by 11 | Viewed by 2267
Abstract
We propose an unmanned aerial vehicle (UAV) failure detection system as the first step of a three-step autonomous emergency landing safety framework for UAVs. We showed the effectiveness and feasibility of using vibration data with the k-means clustering algorithm in detecting mid-flight UAV [...] Read more.
We propose an unmanned aerial vehicle (UAV) failure detection system as the first step of a three-step autonomous emergency landing safety framework for UAVs. We showed the effectiveness and feasibility of using vibration data with the k-means clustering algorithm in detecting mid-flight UAV failures for that purpose. Specifically, we measured vibration signals for different faulty propeller cases during several test flights, utilizing a custom-made hardware system. After we made the vibration graphs and extracted the data, we investigated to determine the combination of acceleration and gyroscope parameters that results in the best accuracy of failure detection in quadcopter UAVs. Our investigations show that considering the gyroscope parameter in the vertical direction (gZ) along with the accelerometer parameter in the same direction (aZ) results in the highest accuracy of failure detection for the purpose of emergency landing of faulty UAVs, while ensuring a quick detection and timely engagement of the safety framework. Based on the parameter set (gZ-aZ), we then created scatter plots and confusion matrices, and applied the k-means clustering algorithm to the vibration dataset to classify the data into three health state clusters—normal, faulty, and failure. We confirm the effectiveness of the proposed system with flight experiments, in which we were able to detect faults and failures utilizing the aforementioned clusters in real time. Full article
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19 pages, 3426 KiB  
Article
Multiple-Actuator Fault Isolation Using a Minimal 1-Norm Solution with Applications in Overactuated Electric Vehicles
by Jinseong Park and Youngjin Park
Sensors 2022, 22(6), 2144; https://doi.org/10.3390/s22062144 - 10 Mar 2022
Viewed by 1351
Abstract
A multiple-actuator fault isolation approach for overactuated electric vehicles (EVs) is designed with a minimal 1-norm solution. As the numbers of driving motors and steering actuators increase beyond the number of controlled variables, an EV becomes an overactuated system, which exhibits [...] Read more.
A multiple-actuator fault isolation approach for overactuated electric vehicles (EVs) is designed with a minimal 1-norm solution. As the numbers of driving motors and steering actuators increase beyond the number of controlled variables, an EV becomes an overactuated system, which exhibits actuator redundancy and enables the possibility of fault-tolerant control (FTC). On the other hand, an increase in the number of actuators also increases the possibility of simultaneously occurring multiple faults. To ensure EV reliability while driving, exact and fast fault isolation is required; however, the existing fault isolation methods demand high computational power or complicated procedures because the overactuated systems have many actuators, and the number of simultaneous fault occurrences is increased. The method proposed in this paper exploits the concept of sparsity. The underdetermined linear system is defined from the parity equation, and fault isolation is achieved by obtaining the sparsest nonzero component of the residuals from the minimal 1-norm solution. Therefore, the locations of the faults can be obtained in a sequence, and only a consistently low computational load is required regardless of the isolated number of faults. The experimental results obtained with a scaled-down overactuated EV support the effectiveness of the proposed method, and a quantitative index of the sparsity condition for the target EV is discussed with a CarSim-connected MATLAB/Simulink simulation. Full article
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19 pages, 2777 KiB  
Article
Adaptive Sliding Mode Fault Compensation for Sensor Faults of Variable Structure Hypersonic Vehicle
by Kai-Yu Hu, Chunxia Yang and Wenjing Sun
Sensors 2022, 22(4), 1523; https://doi.org/10.3390/s22041523 - 16 Feb 2022
Cited by 10 | Viewed by 1534
Abstract
This paper investigates the sensor fault detection and fault-tolerant control (FTC) technology of a variable-structure hypersonic flight vehicle (HFV). First, an HFV nonlinear system considering sensor compound faults, disturbance, and the variable structure parameter is established, which is divided into the attitude angle [...] Read more.
This paper investigates the sensor fault detection and fault-tolerant control (FTC) technology of a variable-structure hypersonic flight vehicle (HFV). First, an HFV nonlinear system considering sensor compound faults, disturbance, and the variable structure parameter is established, which is divided into the attitude angle outer and angular rate inner loops. Then a nonlinear fault integrated detector is proposed to detect the moment of fault occurrence and provide the residual to design the sliding mode equations. Furthermore, the sliding mode method combined with the virtual adaptive controller constitutes the outer loop FTC scheme, and the adaptive dynamic surface combined with the disturbance estimation constitutes the inner loop robust controller; these controllers finally realize the direct compensation of the compound sensor faults under the disturbance condition. This scheme does not require fault isolation and diagnosis observer loops; it only uses a variable structure FTC with a direct estimation algorithm and integrated residual to complete the self-repairing stable flight of variable-structure HFV, which exhibits a high reliability and quick response. Lyapunov theory proved the stability of the system, and numerical simulation proved the effectiveness of the FTC scheme. Full article
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17 pages, 1595 KiB  
Article
Actuator Fault Detection for Unmanned Ground Vehicles Considering Friction Coefficients
by Gyujin Na and Yongsoon Eun
Sensors 2021, 21(22), 7674; https://doi.org/10.3390/s21227674 - 18 Nov 2021
Cited by 4 | Viewed by 1655
Abstract
This paper proposes an actuator fault detection method for unmanned ground vehicle (UGV) dynamics with four mecanum wheels. The actuator fault detection method is based on unknown input observers for linear parameter varying systems. The technical novelty of current work compared to similar [...] Read more.
This paper proposes an actuator fault detection method for unmanned ground vehicle (UGV) dynamics with four mecanum wheels. The actuator fault detection method is based on unknown input observers for linear parameter varying systems. The technical novelty of current work compared to similar work in the literature is that wheel frictions are directly taken into account in the dynamics of UGV, and unknown input observers are developed accordingly. Including the wheel friction, the vehicle dynamics are in the form of linear parameter varying systems. Friction estimation is also discussed in this work, and the effect of friction mismatch was quantitatively investigated by simulations. The effectiveness of proposed method was evaluated under various operation scenarios of the UGV. Full article
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