Actuator Fault Detection Research Articles

Residual based tilt tri‐rotor UAV actuator fault detection using TSK fuzzy model

AbstractUndetected actuator faults on tilt tri‐rotor UAVs can lead to system failures and uncontrolled crashes. Multiple flight modes result in complex models with strong nonlinearity, making fault detection of their actuators a very challenging task. To address this issue, this article proposes a fault detection method based on residual generated by using TSK fuzzy model. Initially, the flight modes of the tilt tri‐rotor UAV are modeled as the TSK fuzzy model. Following this, the residual generator is employed for rapid detection of actuator failures. To enhance detection accuracy, the kernel principal component analysis (KPCA) algorithm is used for a secondary confirmation. The proposed algorithm was validated using both a simulation platform and real flight data. The results demonstrate that the fault detection algorithm achieves high accuracy and real‐time performance, with a computing time of approximately 41 ms in real controller hardware, thus meeting the requirements of practical applications.

Multi-modal Neural Adaptive Observer for Sensor and Actuator Fault Detection and Identification

Multi-modal Neural Adaptive Observer for Sensor and Actuator Fault Detection and Identification

Practical actuator fault detection, identification, and accommodation for a quadrotor with arbitrary faults through direct computation of thrusts

Practical actuator fault detection, identification, and accommodation for a quadrotor with arbitrary faults through direct computation of thrusts

Data-Driven Fault Detection and Isolation for Multirotor System Using Koopman Operator

This paper presents a data-driven fault detection and isolation (FDI) for a multirotor system using Koopman operator and Luenberger observer. Koopman operator is an infinite-dimensional linear operator that can transform nonlinear dynamical systems into linear ones. Using this transformation, our aim is to apply the linear fault detection method to the nonlinear system. Initially, a Koopman operator-based linear model is derived to represent the multirotor system, considering factors like non-diagonal inertial tensor, center of gravity variations, aerodynamic effects, and actuator dynamics. Various candidate lifting functions are evaluated for prediction performance and compared using the root mean square error to identify the most suitable one. Subsequently, a Koopman operator-based Luenberger observer is proposed using the lifted linear model to generate residuals for identifying faulty actuators. Simulation and experimental results demonstrate the effectiveness of the proposed observer in detecting actuator faults such as bias and loss of effectiveness, without the need for an explicitly defined fault dataset.

Actuator fault diagnosis and severity identification of turbofan engines for steady-state and dynamic conditions

Actuator faults can be critical in turbofan engines as they can lead to stall, surge, loss of thrust and failure of speed control. Thus, fault diagnosis of gas turbine actuators has attracted considerable attention, from both academia and industry. However, the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation. In addition, previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time, especially when these are accompanied by sudden failures under dynamic conditions. This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors. The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation. The maximum error for each actuator is less than 0.06% and 0.07%, with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases, respectively. These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions, even in the case of a sudden malfunction. The research results demonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.

The distributed fault diagnosis for nonlinear complex dynamical networks based on [formula omitted]-detection criterion

The actuator fault diagnosis problem of nonlinear complex dynamical networks with stochastic noise is investigated. Firstly, a local estimation based on a priori knowledge and measurement output is designed to estimate state for each node. A sufficient condition is derived such that the estimation error is stable and the estimation gain matrix is obtained in fault-free case by applying the stability theory. Secondly, considering additive actuator fault in presence of process and measurement noises, the residual signal based on estimation error is designed to detect faults and locate them. Thirdly, the threshold is acquired by applying the covariance matrix of estimation error. A fault detection and isolation logic is designed via the σ-detection criterion. Finally, two different examples are designed to verify the effectiveness of the designed actuator fault detection and isolation scheme.

Resilience enhancement to loss of actuator effectiveness in a Model-Free Adaptive framework

Within the theoretical framework supporting the Model Free Adaptive Control approach, this paper investigates on the presence of possible loss of effectiveness affecting the actuator of a Single-Input Single-Output plant. In particular, stemming from a tracking controller proposed in a seminal paper and the related proofs, an identification algorithm is proposed for the estimation of the loss of effectiveness, with proved bounded estimation error, along with a condition for the detection of actuator faults. A fault-resilient tracking controller is finally proposed, based on the estimated loss of effectiveness, providing asymptotically vanishing tracking error. A comparative analysis by simulation on a benchmark system taken from the pertinent literature is also presented to validate the proposed development.

Vision-Guided Tracking and Emergency Landing for UAVs on Moving Targets

This paper presents a vision-based adaptive tracking and landing method for multirotor Unmanned Aerial Vehicles (UAVs), designed for safe recovery amid propulsion system failures that reduce maneuverability and responsiveness. The method addresses challenges posed by external disturbances such as wind and agile target movements, specifically, by considering maneuverability and control limitations caused by propulsion system failures. Building on our previous research in actuator fault detection and tolerance, our approach employs a modified adaptive pure pursuit guidance technique with an extra adaptation parameter to account for reduced maneuverability, thus ensuring safe tracking of moving objects. Additionally, we present an adaptive landing strategy that adapts to tracking deviations and minimizes off-target landings caused by lateral tracking errors and delayed responses, using a lateral offset-dependent vertical velocity control. Our system employs vision-based tag detection to ascertain the position of the Unmanned Ground Vehicle (UGV) in relation to the UAV. We implemented this system in a mid-mission emergency landing scenario, which includes actuator health monitoring of emergency landings. Extensive testing and simulations demonstrate the effectiveness of our approach, significantly advancing the development of safe tracking and emergency landing methods for UAVs with compromised control authority due to actuator failures.

State estimation based actuator fault detection for tandem rotor helicopter with experimental validation

Timely fault detection is essential to flight safety of tandem rotor helicopter. In this paper, a fault detection method is proposed based on state estimation and the deviation between the measured value and the estimated value. Firstly, a polyhedral linear parameter varying (LPV) model is established to solve the nonlinear coupling problem of the helicopter in different moving directions. Then, to obtain actuator fault information, a robust state observer is developed to generate the residual. Meanwhile, a mixed [Formula: see text] algorithm is designed to calculate observer gain by configuring performance parameters, which makes the residuals both sensitive to actuator faults and robust to external disturbances. Furthermore, a dynamic threshold method is introduced to evaluate the residual signal for actuator fault detection. Finally, based on the 3-dof helicopter platform, the effectiveness of the proposed method is verified through comparative simulation and experimental tests.

Actuator fault detection and isolation in a class of nonlinear interconnected systems

In this paper, the problem of actuator fault detection and isolation is investigated for a class of nonlinear interconnected large-scale systems with modelling uncertainty and measurement noise, where each subsystem can have multiple inputs and multiple outputs (MIMO). The main contribution of this work is the derivation of a scheme that is able to diagnose single or multiple actuator faults in one or multiple subsystems. Each subsystem is monitored by a local diagnosis agent which contains the actuator fault detection module and the isolation module. The detection threshold in the fault detection module is generated through the use of a novel filtering technique, while the fault isolation module is realised by applying a reasoning-based decision logic based on a fault signature matrix. Fault propagation among subsystems is investigated and the results obtained allow for the identification of the subsystems that contain the faulty actuators. Finally, the effectiveness of the proposed actuator fault diagnosis method is demonstrated through a simulation example.

Observer based fault diagnosis of aircraft active landing gear system

A landing gear system is vital in aircraft landing and taxiing. In this paper, an observer-based fault detection and fault reconstruction means is adopted for the aircraft’s active landing gear. Firstly, a six-degree-of-freedom (6-DOF) landing gear model is established by analysing the dynamics of the fuselage and landing gears. By analysing the fault mechanism of actuators and sensors, the fault model of actuators and sensors is established. Secondly, according to the above model, the unknown input observer (UIO) is designed to obtain the residuals, and the fault detection of actuators and sensors is realized according to the comparison between residuals and thresholds. On this basis, an adaptive unknown input observer (AUIO) is designed, through which the state values and fault values can be reconstructed. Finally, simulation experiments are tested under different fault types to prove the validity of the fault detection and reconstruction based on UIO and AUIO.

Novel Methodology for Integrated Actuator and Sensors Fault Detection and Estimation in an Active Suspension System

Novel Methodology for Integrated Actuator and Sensors Fault Detection and Estimation in an Active Suspension System

Aeroengine Actuator Fault Detection and Estimation Via Combined Model Observers

Accurate diagnosis of faults in aeroengines is crucial for enhancing reliability and reducing operational and maintenance costs. This paper proposes a fault diagnosis approach for aeroengine actuators that utilizes a combination of model observers. Firstly, the faults of aeroengine actuators are described, and their representation as Aero-engine State Variable Model (SVM) is elaborated on. Secondly, a Luneberger model observer is designed to detect actuator faults using observer residuals. Thirdly, Adaptive Thau Observers (ATO) are developed to accurately estimate the offset value of actuator faults. Finally, the proposed fault diagnostics solution is tested and validated using a benchmark aeroengine model-CMAPSS 40. Fault diagnostics results under two fault scenarios demonstrate the Effectiveness of the proposed approach.

An Improved Zonotopic Approach Applied to Fault Detection for Takagi–Sugeno Fuzzy Systems

In this work, an actuator fault detection problem for discrete-time Takagi-Sugeno fuzzy systems is tackled in a bounded error context where both state disturbances and measurement noise are assumed to be unknown but bounded with known bounds. First, a peak-to-peak performance synthesis method is applied to design a robust residual generator against the considered process disturbances and measurement noise. Meanwhile, an improved zonotopic approach is proposed to compute tight adaptive thresholds for residual evaluation. Then, a reliable set-membership fault detection strategy with the aid of generated residual signals and adaptive thresholds is introduced. Finally, the viability of the proposed method is demonstrated via a numerical simulation. Then, an experimentation on a 3D Crane system is performed to show its practicability.

Comparison of structured residuals design techniques for actuator and sensor fault detection and isolation in a permanent magnet DC motor

Comparison of structured residuals design techniques for actuator and sensor fault detection and isolation in a permanent magnet DC motor

Reinforced unscented Kalman filter for consensus achievement of uncertain multi‐agent systems subject to actuator faults

AbstractIn this paper, actuator fault detection and reconstruction in consensus tracking of uncertain multi‐agent systems (MAS) is addressed. The communication is assumed to be connected undirected. An adaptive fault detection method is developed to detect actuator faults. A novel‐reinforced unscented Kalman filter (RUKF) is employed to reconstruct the faults by adjusting the noise covariance matrices of unscented Kalman filter (UKF) as well as to train neural network internal parameters by providing a set of previous measurements. A Chebyshev neural network (CNN) is incorporated to learn the uncertain plant. To prevent the neural network approximation errors a hyperbolic tangent function‐based robust control term is applied. The Lyapunov stability approach guarantees the stability of the proposed RUKF, which runs in conjunction with robust control method. Lastly, numerical simulations are presented to show the effectiveness of the proposed RUKF under actuator abrupt, intermittent, and transient fault conditions.

Hybrid robust fault detection and isolation of satellite reaction wheel actuators

In this paper, a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults, external disturbances, and parametric uncertainties. The proposed methodology incorporates a residual generation module, including a bank of filters, into an intelligent residual evaluation module. First, residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances. The residual evaluation module is developed based on the suggested series and parallel forms. Further, a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance. A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances, manoeuvres, uncertainties, and noises. The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.

Switching-based adaptive fault-tolerant control for uncertain nonlinear systems against actuator and sensor faults

In previous research on fault switching of redundant actuators using monitoring modules, the interference of sensor faults on monitoring results has not received enough attention. In this paper, a new fault-tolerant control (FTC) method combining adaptive compensation and active switching is proposed for a class of uncertain nonlinear systems with backup actuators against actuator and sensor faults. While using command filtered backstepping to reduce computational burden, a direct adaptive compensation scheme is designed to ensure that sensor faults within tolerable bounds do not trigger actuator switching. Based on this, a new lumped monitoring function (MF) containing prescribed performances and tolerable sensor fault bounds is designed to detect and isolate faulty actuators and ensure that the tracking error always satisfies prescribed transient and steady-state performances. Two simulation cases are used for illustrating the proposed method. It is proved that the proposed method can effectively reduce the interference of sensor faults on actuator fault detection and improve the accuracy of actuator switching.

Ensemble Transfer Learning Based Cross-Domain UAV Actuator Fault Detection

The actuator is critical for controlling the unmanned aerial vehicle (UAV) attitude. With the development of sensing technology, the convenience of collecting high-quality data by sensors has provided favorable conditions for data-driven based UAV actuator fault detection methods. However, there are less historical data on a new type of UAV, making it challenging to accurately obtain data-driven fault detection models. The data domain distribution of the new UAV (target domain) is different from that of existing UAVs (source domains), and the basic data-driven methods are difficult to achieve cross-domain fault detection. The transfer learning of a single source domain method can solve this problem, but it has a strong dependence on the single source domain data characteristics. Therefore, we propose the ensemble transfer learning method, which learns multiple UAV data characteristics to address the accuracy degradation issue caused by single-source domain insufficient characteristics and realize cross-domain actuator fault detection with limited data for the new type of UAV. In the method, the basic actuator prediction models are established based on the bidirectional long short-term memory (BiLSTM) algorithm. Then we combine the transfer learning method to realize the cross-domain fault detection model establishment of the new type of UAV. In addition, a cross-domain similarity metric based on R-score is creatively proposed for the ensemble to avoid excessive dependence on a single-source domain. We conduct experiments based on the ArduPilot open-source simulation platform, and the results show that our proposed method can adaptively obtain more accurate fault detection performance.

Robust actuator fault detection for quadrotor UAV with guaranteed sensitivity

This paper proposes an actuator fault detection method for quadrotor UAV, in which sensitivity to fault and robustness to disturbance are simultaneously guaranteed. First, a quadrotor UAV system is converted to a linear-parameter-varying (LPV) form related to the altitude velocity and attitude angular velocity. Secondly, based on this LPV model, a fault detection method is designed, in which H− performance is applied to enhance fault sensitivity and L∞ performance is applied for guarantee robustness of the residual against the disturbance. And then the L∞ performance provides a detection threshold combined with initial flight condition for residual evaluation. Finally, based on the QDrone testbed, both numerical simulations and plentiful online experiments are implemented to validate the performance of the proposed fault detection scheme.