Aircraft sensor fault detection using state and input estimation

Ansari, Ahmad; Bernstein, Dennis S.

doi:10.1109/acc.2016.7526603

Cited by 15 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In past decades, many new Fault Detection methods were introduced in response to the concerns for the safety of aircraft operations. Many of these methods target only specific types of aircraft: Kuric et al [2] and Han et al [3] present approaches for use in multirotors; Qi et al [4], [5] review different methods for Fault Diagnosis in helicopters; Melody et al [6] and Ansari et al [7] describe methods for icing detection and sensor fault detection in fixed-wings, respectively.…”

Section: Introductionmentioning

confidence: 99%

Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles

Keipour

Mousaei

Scherer

2019

2019 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

The recent increase in the use of aerial vehicles raises concerns about the safety and reliability of autonomous operations. There is a growing need for methods to monitor the status of these aircraft and report any faults and anomalies to the safety pilot or to the autopilot to deal with the emergency situation. In this paper, we present a real-time approach using the Recursive Least Squares method to detect anomalies in the behavior of an aircraft. The method models the relationship between correlated input-output pairs online and uses the model to detect the anomalies. The result is an easy-to-deploy anomaly detection method that does not assume a specific aircraft model and can detect many types of faults and anomalies in a wide range of autonomous aircraft. The experiments on this method show a precision of 88.23%, recall of 88.23% and 86.36% accuracy for over 22 flight tests. The other contribution is providing a new fault detection open dataset for autonomous aircraft, which contains complete data and the ground truth for 22 fixed-wing flights with eight different types of mid-flight actuator failures to help future fault detection research for aircraft.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles

Keipour

Mousaei

Scherer

2019

2019 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

show abstract

“…Moreover, the normal EKF will suffer when system non-linearity increases, which may affect the accuracy of the estimation. Some researchers have proposed the EKF-based analytical method of aircraft sensor fault detection (23,24) . However, these methods require complex calculations, which may increase computation time.…”

Section: Introductionmentioning

confidence: 99%

An innovative analytic redundancy approach to air data sensor fault detection

Prabhu

Anitha

2019

Aeronaut. j.

View full text Add to dashboard Cite

This 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.

show abstract