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1
Alsuwian, Turki, Arslan Ahmed Amin, Muhammad Taimoor Maqsood, Muhammad Bilal Qadir, Saleh Almasabi, and Mohammed Jalalah. "Advanced Fault-Tolerant Anti-Surge Control System of Centrifugal Compressors for Sensor and Actuator Faults." Sensors 22, no. 10 (2022): 3864. http://dx.doi.org/10.3390/s22103864.
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Faults frequently occur in the sensors and actuators of process machines to cause shutdown and process interruption, thereby creating costly production loss. centrifugal compressors (CCs) are the most used equipment in process industries such as oil and gas, petrochemicals, and fertilizers. A compressor control system called an anti-surge control (ASC) system based on many critical sensors and actuators is used for the safe operation of CCs. In this paper, an advanced active fault-tolerant control system (AFTCS) has been proposed for sensor and actuator faults of the anti-surge control system of a centrifugal compressor. The AFTCS has been built with a dedicated fault detection and isolation (FDI) unit to detect and isolate the faulty part as well as replace the faulty value with the virtual redundant value from the observer model running in parallel with the other healthy sensors. The analytical redundancy is developed from the mathematical modeling of the sensors to provide estimated values to the controller in case the actual sensor fails. Dual hardware redundancy has been proposed for the anti-surge valve (ASV). The simulation results of the proposed Fault-tolerant control (FTC) for the ASC system in the experimentally validated CC HYSYS model reveal that the system continued to operate in the event of faults in the sensors and actuators maintaining system stability. The proposed FTC for the ASC system is novel in the literature and significant for the process industries to design a highly reliable compressor control system that would continue operation despite faults in the sensors and actuators, hence preventing costly production loss.
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Mohammad, Waseem. "DIAGNOSIS OF ACTIVE MAGNETIC BEARING SYSTEM FAULTS USING PRINCIPAL COMPONENT ANALYSIS." International Journal of Advances in Engineering & Scientific Research 2, no. 1 (2015): 36–42. https://doi.org/10.5281/zenodo.10725506.
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<strong>ABSTRACT</strong> <strong> </strong> <em>Active Magnetic Bearings (AMBs) endure rotating member without any frictional loss. Therefore compared to the journal and hydrodynamic bearings, AMBs have benefits of reduced frictional losses as a result they can support high speed rotor. Stable working operation of AMB is principally depends on the working condition of its position sensors, actuators and controller. Therefore sensors and actuators are the important working elements of AMB system. Fault or failure in any one the sensor or actuator of AMB system can results in undesired rotor dynamics behaviour. Hence to ensure the safe operation and authentic performance of AMB system, fault detection and diagnosis (FDD) of sensors and actuators is very much essential. Principal component analysis (PCA) is a model free and robust statistical technique which can detect and diagnose the faults in engineering systems with high accuracy. Therefore, in the present work, PCA based FDD methodology is employed to detection and diagnosis of AMB sensors and actuators fault. Simulations have been carried out to diagnose the sensor and actuator faults of AMB system. Q-statistic or square prediction error is used for bias and noise faults.</em> <strong><em>Keywords</em></strong><em>: Active Magnetic Bearing (AMB), Fault detection and diagnosis (FDD), Principal component analysis (PCA), Sensors and actuators faults.</em>
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Amin, Arslan Ahmed, and Khalid Mahmood-ul-Hasan. "Hybrid fault tolerant control for air–fuel ratio control of internal combustion gasoline engine using Kalman filters with advanced redundancy." Measurement and Control 52, no. 5-6 (2019): 473–92. http://dx.doi.org/10.1177/0020294019842593.
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In this paper, a hybrid fault tolerant control system is proposed for air–fuel ratio control of internal combustion gasoline engines based on Kalman filters and triple modular redundancy. Hybrid fault tolerant control system possesses properties of both active fault tolerant control system and passive fault tolerant control system. As part of active fault tolerant control system, fault detection and isolation unit is designed using Kalman filters to provide estimated values of the sensors to the engine controller in case of faults in the sensors. As part of passive fault tolerant control system, a dedicated proportional–integral feedback controller is incorporated to maintain air–fuel ratio by adjusting the throttle actuator in the fuel supply line in faulty and noisy conditions for robustness to faults and sensors’ noise. Redundancy is proposed in the sensors and actuators as a simultaneous failure of more than one sensor, and failure of the single actuator will cause the engine shutdown. Advanced redundancy protocol triple modular redundancy is proposed for the sensors and dual redundancy is proposed for actuators. Simulation results in the MATLAB Simulink environment show that the proposed system remains stable during faults in the sensors and actuators. It also maintains air–fuel ratio without any degradation in the faulty conditions and is robust to noise. Finally, the probabilistic reliability analysis of the proposed model is carried out. The study shows that the proposed hybrid fault tolerant control system with redundant components presents a novel and highly reliable solution for the air–fuel ratio control in internal combustion engines to prevent engine shutdown and production loss for greater profits.
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Ye, Zhengyu, Yuehua Cheng, Ziquan Yu, and Bin Jiang. "Distributed Adaptive Fault-Tolerant Control for Leaderless/Leader–Follower Multi-Agent Systems against Actuator and Sensor Faults." Electronics 12, no. 13 (2023): 2924. http://dx.doi.org/10.3390/electronics12132924.
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The faults of actuators and sensors can lead to abnormal operations or even system faults in multi-agent systems (MASs). To address this issue, this paper proposes an adaptive fault-tolerant control (FTC) algorithm for leaderless/leader–follower MASs against actuator and sensor faults. First, extended states integrating the fault components are constructed and the MAS is transformed into a descriptor system form. Then, a sliding-mode observer is designed for the transformed MAS. Based on the estimated MAS states and faults, adaptive FTC algorithms are developed, which update the control gains with the distributed tracking error. Finally, numerical simulations demonstrate that the proposed method can guarantee MAS stability against actuator and sensor faults.
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Rosen, C., L. Rieger, U. Jeppsson, and P. A. Vanrolleghem. "Adding realism to simulated sensors and actuators." Water Science and Technology 57, no. 3 (2008): 337–44. http://dx.doi.org/10.2166/wst.2008.130.
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In this paper, we propose a statistical theoretical framework for incorporation of sensor and actuator faults in dynamic simulations of wastewater treatment operation. Sensor and actuator faults and failures are often neglected in simulations for control strategy development and testing, although it is well known that they represent a significant obstacle for realising control at full-scale facilities. The framework for incorporating faults and failures is based on Markov chains and displays the appealing property of easy transition of sensor and actuator history into a model for fault generation. The paper briefly describes Markov theory and how this is used together with models for sensor and actuator dynamics to achieve a realistic simulation of measurements and actuators.
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Stetter, Ralf. "A Fuzzy Virtual Actuator for Automated Guided Vehicles." Sensors 20, no. 15 (2020): 4154. http://dx.doi.org/10.3390/s20154154.
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In the last decades, virtual sensors have found increasing attention in the research community. Virtual sensors employ mathematical models and different sources of information such as actuator states or sensors, which are already existing in a system, in order to generate virtual measurements. Additionally, in recent years, the concept of virtual actuators has been proposed by leading researchers. Virtual actuators are parts of a fault-tolerant control strategy and aim to accommodate faults and to achieve a safe operation of a faulty plant. This paper describes a novel concept for a fuzzy virtual actuator applied to an automated guided vehicle (AGV). The application of fuzzy logic rules allows integrating expert knowledge or experimental data into the decision making of the virtual actuator. The AGV under consideration disposes of an innovative steering concept, which leads to considerable advantages in terms of maneuverability, but requires an elaborate control system. The application of the virtual actuator allows the accommodation of several possible faults, such as a slippery surface under one of the drive modules of the AGV.
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Liu, Xiaofeng, Chenshuang Luo, and Liuqi Xiong. "Design of an Improved Hybrid FTC for Faults in Aero-Engine Closed-Loop Control System." International Journal of Aerospace Engineering 2021 (September 16, 2021): 1–19. http://dx.doi.org/10.1155/2021/3553461.
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Due to the aero-engines in a high temperature, pressure, and spool speed environment, the concurrent faults in actuator/sensor combined with degradation of the aero-engine could arise the problem of closed-loop instability during the flight. For this purpose, an improved hybrid fault-tolerant control (FTC) technique has been studied in this paper to deal with simultaneous failure of actuators and sensors associated with health parameters of the typical components’ degradation in aero-engines. The improved hybrid FTC structure combined with the nonlinear thermodynamic component-level (NCL) model-based estimation method merges fault estimation of actuators/sensors and typical components’ degradation estimation process into the FTC process. A robust H ∞ state feedback controller under the disturbance of simultaneous actuator and sensor faults is designed in the proposed method, together with the switching algorithm serving for the fault estimation and improved hybrid FTC channels. In order to show the feasibility of the proposed method, several semiphysical experiments are engaged to illustrate that the improved hybrid FTC structure can save the tolerant-control time and improve performance of the control system.
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Mokhtari, Sohrab, Alireza Abbaspour, Kang K. Yen, and Arman Sargolzaei. "Neural Network-Based Active Fault-Tolerant Control Design for Unmanned Helicopter with Additive Faults." Remote Sensing 13, no. 12 (2021): 2396. http://dx.doi.org/10.3390/rs13122396.
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A novel adaptive neural network-based fault-tolerant control scheme is proposed for six degree-of-freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate actuators and sensors’ faults in real time. An adaptive observer-based on neural network (NN) and extended Kalman filter (EKF) is designed, which incorporates the helicopter’s dynamic model to detect faults in the actuators and navigation sensors. Based on the detected faults, an active fault-tolerant controller, including three loops of dynamic inversion, is designed to compensate for the occurred faults in real time. The simulation results showed that the proposed approach is able to detect and mitigate different types of faults on the helicopter actuators, and the helicopter tracks the desired trajectory without any interruption.
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Taherkhani, Ashkan, Farhad Bayat, Kaveh Hooshmandi, and Andrzej Bartoszewicz. "Generalized Sliding Mode Observers for Simultaneous Fault Reconstruction in the Presence of Uncertainty and Disturbance." Energies 15, no. 4 (2022): 1411. http://dx.doi.org/10.3390/en15041411.
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In this paper, a generalized sliding mode observer design method is proposed for the robust reconstruction of sensors and actuators faults in the presence of both unknown disturbances and uncertainties. For this purpose, the effect of uncertainty and disturbance on the system has been considered in generalized state-space form, and the LMI tool is combined with the concept of an equivalent output error injection method to reduce the effects of them on the reconstruction process. The upper bound of the disturbance and uncertainty are minimized in the design of the sliding motion so that the reconstruction of the faults will be minimized. The design method is applied for actuator faults in the generalized state-space form, and then with some suitable filtering, the method extends as sensors and actuators coincidentally faults. Since in the proposed approach, the state trajectories do not leave the sliding manifold even in simultaneous sensors and actuators faults, then the faults are reconstructed based upon information retrieved from the equivalent output error injection signal. Due to the importance of the robust fault reconstruction in the wind energy conversion system (WECS), the proposed approach is successfully applied to a 5 MW wind turbine system. The simulation results verify the robust performances of the proposed approach in the presence of unknown perturbations and uncertainties.
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Zhang, Yang, Rulin Zhou, Lingyu Meng, Jian Shi, and Kaixian Ba. "Steady-State Fault Propagation Characteristics and Fault Isolation in Cascade Electro-Hydraulic Control System." Machines 12, no. 9 (2024): 600. http://dx.doi.org/10.3390/machines12090600.
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Model-based fault diagnosis serves as a powerful technique for addressing fault detection and isolation issues in control systems. However, diagnosing faults in closed-loop control systems is more challenging due to their inherent robustness. This paper aims to detect and isolate actuator and sensor faults in the cascade electro-hydraulic control system of a turbofan engine. Based on the fault characteristics, we design a robust unknown perturbation decoupling residual generator and an optimal fault observer specifically for the inner and outer control loops to detect potential faults. To locate the faults, we analyze the steady-state propagation laws of actuator and sensor faults within the loops using the final value theorem. Based on this, we establish the minimal-dimensional fault influence distribution matrix specific to the cascade turbofan engine control system. Subsequently, we construct the normalized residual vectors and monitor its vector angles against each row of the fault influence distribution matrix to isolate faults. Experiments conducted on an electro-hydraulic test bench demonstrate that our proposed method can accurately locate four typical faults of actuators and sensors within the cascade electro-hydraulic control system. This study enriches the existing fault isolation methods for complex dynamic systems and lays the foundation for guiding component repair and maintenance.
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Sun, Jian, Zewen Li, and Minsheng Yang. "Multiple Fault-Tolerant Control of DC Microgrids Based on Sliding Mode Observer." Electronics 14, no. 5 (2025): 931. https://doi.org/10.3390/electronics14050931.
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Different locations and types of faults affect the safe and reliable operation of DC microgrids. Therefore, this paper proposes a secondary multiple fault-tolerant control scheme for a DC microgrid based on a sliding mode observer to ensure the voltage is restored to the rated value and realize the proportional current sharing of all sources. Firstly, the secondary control model of the DC microgrid is established, considering the multiple faults of actuators and sensors simultaneously. Secondly, the system model is transformed into two subsystems by bilinear coordinate transformation, and multiple faults decoupling between the sensor and actuator is realized. Then, two sliding mode observers are designed for the two transformed subsystems. The sliding mode variable structure equivalent principle is used to reconstruct the faults at different positions without knowing the fault models in advance, which is convenient for subsequent processing. Then, the fault-tolerant controller based on the sliding mode observer is designed, which uses the reconstructed value to offset the influence of sensor and actuator faults on the DC microgrid and realizes the fault-tolerant control of the DC microgrid. Finally, the effectiveness of the proposed control strategy is verified by experiments.
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Kiamanesh, Bahareh, Ali Behravan, and Roman Obermaisser. "Fault Injection with Multiple Fault Patterns for Experimental Evaluation of Demand-Controlled Ventilation and Heating Systems." Sensors 22, no. 21 (2022): 8180. http://dx.doi.org/10.3390/s22218180.
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Heating, ventilation, and air-conditioning (HVAC) systems are large-scale distributed systems that can be subject to multiple faults affecting the electronics, sensors, and actuators, potentially causing high energy consumption, occupant discomfort, degraded indoor air quality and risk to critical infrastructure. Fault injection (FI) is an effective experimental method for the validation and dependability evaluation of such HVAC systems. Today’s FI frameworks for HVAC systems are still based on a single fault hypothesis and do not provide insights into dependability in the case of multiple faults. Therefore, this paper presents modeling patterns of numerous faults in HVAC systems based on data from field failure rates and maintenance records. The extended FI framework supports the injection of multiple faults with exact control of the timing, locality, and values in fault-injection vectors. A multi-dimensional fault model is defined, including the probability of the occurrence of different sensor and actuator faults. Comprehensive experimental results provide insights into the system’s behavior for concrete example scenarios using patterns of multiple faults. The experimental results serve as a quantitative evaluation of key performance indicators (KPI) such as energy efficiency, air quality, and thermal comfort. For example, combining a CO2 sensor fault with a heater actuator fault increased energy consumption by more than 70%.
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Antic, Sanja, Vanja Lukovic, and Zeljko Djurovic. "Expert system for FDI of dc motor faults using structured residuals design technique." Serbian Journal of Electrical Engineering 20, no. 1 (2023): 93–105. http://dx.doi.org/10.2298/sjee2301093a.
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A major concern in many electrical drives is the reliability of sensors and actuators. In the paper, the usage of the Drools expert system (ES) for Fault detection and isolation (FDI) of the additive actuator and sensor DC Motor faults using the Structured residuals design technique (SRDT) is presented. The SRDT is used to obtain essential knowledge about the system. Afterward, an expert system that can isolate faults based on the developed structure matrix and generated residuals is designed. Accordingly, following the structure matrix each residual becomes able to answer to a desired subset of faults and stands insensitive to the others. The proposed method is successfully applied in an analyzed laboratory system and can be used for online FDI.
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Ben Brahim, Ali, Slim Dhahri, Fayçal Ben Hmida, and Anis Sallami. "Sliding Mode Observers Based-Simultaneous Actuator and Sensor Faults Estimation for Linear Parameter Varying Systems." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/1747095.
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This paper proposes a scheme to estimate actuator and sensor faults simultaneously for a class of linear parameter varying system expressed in polytopic structure where its parameters evolve in the hypercube domain. Transformed coordinate system design is adopted to decouple faults in actuators and sensors during the course of the system’s operation coincidentally, and then two polytopic subsystems are constructed. The first subsystem includes the effect of actuator faults but is free from sensor faults and the second one is affected only by sensor faults. The main contribution is to conceive two polytopic sliding mode observers in order to estimate the system states and actuator and sensor faults at the same time. Meanwhile, in linear matrix inequality optimization formalism, sufficient conditions are derived withH∞performances to guarantee the stability of estimation error and to minimize the effect of disturbances. Therefore, all parameters of observers can be designed by solving these conditions. Finally, simulation results are given to illustrate the effectiveness of the proposed simultaneous actuator and sensor faults estimation.
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Zhou, Yan, Huiying Liu, and Huijuan Guo. "L1 Adaptive Fault-Tolerant Control for Nonlinear Systems Subject to Input Constraint and Multiple Faults." Actuators 13, no. 7 (2024): 258. http://dx.doi.org/10.3390/act13070258.
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This paper investigates an L1 adaptive fault-tolerant control scheme for nonlinear systems with input constraint, external disturbances, and multiple faults, which include actuator faults and sensor faults. Faults and input constraint are important factors that affect the stability and performance of a control system. Actuators and sensors are the most vulnerable components, with the former receiving more attention in comparison. In this paper, sensor faults are first transformed into pseudo-actuator faults through the augmented matrix approach, which facilitates their handling together with actuator faults. Saturation constraints on the control signal are not conducive to the design of the controller. The conversion of an input-saturated function to a time-varying linear system is completed based on function approximation and Lagrange’s mean value theorem. Moreover, a nonlinear system with unknown input gain and uncertainties is constructed using these methods. Next, an L1 adaptive fault-tolerant controller is designed to cope with uncertainties, including system uncertainties, external disturbances, faults, and approximation errors. In the L1 adaptive controller, the online estimation of the time-varying parameters allows for updating of the system state, while the combination of the two is transmitted to the control law such that it can compensate for the effects of the uncertainties. The stability and performance boundaries are further derived using the Lyapunov theory and the L1 reference system. Finally, simulations are carried out to demonstrate the effectiveness of the proposed controller.
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Pan, Jinghui, Lili Qu, and Kaixiang Peng. "Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN." Entropy 23, no. 6 (2021): 751. http://dx.doi.org/10.3390/e23060751.
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This paper proposes a data-driven method-based fault diagnosis method using the deep convolutional neural network (DCNN). The DCNN is used to deal with sensor and actuator faults of robot joints, such as gain error, offset error, and malfunction for both sensors and actuators, and different fault types are diagnosed using the trained neural network. In order to achieve the above goal, the fused data of sensors and actuators are used, where both types of fault are described in one formulation. Then, the deep convolutional neural network is applied to learn characteristic features from the merged data to try to find discriminative information for each kind of fault. After that, the fully connected layer does prediction work based on learned features. In order to verify the effectiveness of the proposed deep convolutional neural network model, different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), conventional neural network (CNN) using the LeNet-5 method, and long-term memory network (LTMN) are investigated and compared with DCNN method. The results show that the DCNN fault diagnosis method can realize high fault recognition accuracy while needing less model training time.
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Li, Shiqing, Michael Frey, and Frank Gauterin. "Model-Based Condition Monitoring of the Sensors and Actuators of an Electric and Automated Vehicle." Sensors 23, no. 2 (2023): 887. http://dx.doi.org/10.3390/s23020887.
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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.
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Hashimoto, Masafumi, Yuuki Nakamura, and Kazuhiko Takahashi. "Fault Diagnosis and Fault-Tolerant Control of a Joystick-Controlled Wheelchair." Journal of Robotics and Mechatronics 20, no. 6 (2008): 903–11. http://dx.doi.org/10.20965/jrm.2008.p0903.
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This paper presents a method of fault diagnosis and fault-tolerant control for a nonholonomic powered wheelchair. Hard faults of sensors and actuators in two drive/steering units of the wheelchair are handled. The fault diagnosis is based on the interacting multiple-model (IMM) estimator. In order to improve fault decisions, we implement mode probability averaging and heuristic decision-making rule in the IMM-based algorithm. A fault-tolerant controller designed based on Ackerman geometry enables safe motion of the wheelchair even if sensors and actuators have partially failed. Experimental results verify the proposed method.
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Bououden, Sofiane, Ilyes Boulkaibet, Mohammed Chadli, and Abdelaziz Abboudi. "A Robust Fault-Tolerant Predictive Control for Discrete-Time Linear Systems Subject to Sensor and Actuator Faults." Sensors 21, no. 7 (2021): 2307. http://dx.doi.org/10.3390/s21072307.
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In this paper, a robust fault-tolerant model predictive control (RFTPC) approach is proposed for discrete-time linear systems subject to sensor and actuator faults, disturbances, and input constraints. In this approach, a virtual observer is first considered to improve the observation accuracy as well as reduce fault effects on the system. Then, a real observer is established based on the proposed virtual observer, since the performance of virtual observers is limited due to the presence of unmeasurable information in the system. Based on the estimated information obtained by the observers, a robust fault-tolerant model predictive control is synthesized and used to control discrete-time systems subject to sensor and actuator faults, disturbances, and input constraints. Additionally, an optimized cost function is employed in the RFTPC design to guarantee robust stability as well as the rejection of bounded disturbances for the discrete-time system with sensor and actuator faults. Furthermore, a linear matrix inequality (LMI) approach is used to propose sufficient stability conditions that ensure and guarantee the robust stability of the whole closed-loop system composed of the states and the estimation error of the system dynamics. As a result, the entire control problem is formulated as an LMI problem, and the gains of both observer and robust fault-tolerant model predictive controller are obtained by solving the linear matrix inequalities (LMIs). Finally, the efficiency of the proposed RFTPC controller is tested by simulating a numerical example where the simulation results demonstrate the applicability of the proposed method in dealing with linear systems subject to faults in both actuators and sensors.
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Ismail, M. A. A., and J. Windelberg. "Fault detection of ball screw-based electromechanical actuators using electrical, ultrasound and accelerometer sensors." International Journal of Condition Monitoring 8, no. 2 (2018): 52–57. http://dx.doi.org/10.1784/204764218823029048.
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Electromechanical actuators (EMAs) are considered a promising energy-efficient technology for actuating the flight controls of future aircraft. When it comes to aerospace systems, the degradation of EMAs should be checked during regular maintenance events or through condition-based maintenance. Ball bearings have a significant failure rate for flight control EMAs and are usually monitored by vibration noise. A challenge for detecting bearing faults, using state-of-the-art industrial methods, is the presence of a ball-screw mechanism that produces nominal vibration noise similar to that of faulty bearings. No prior research has investigated this problem. This paper explores vibration noise generated from a set of healthy and faulty bearings included in a typical ball-screw EMA. In addition, a method is introduced for evaluating fault diagnosis performance for different time and frequency vibration features. The technique has been validated on an EMA actuator at the German Aerospace Center (DLR).
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Montie, D., and E. Maslen. "Self-Sensing in Fault Tolerant Magnetic Bearings." Journal of Engineering for Gas Turbines and Power 123, no. 4 (1999): 864–70. http://dx.doi.org/10.1115/1.1383257.
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Several schemes have recently been proposed for achieving either fault tolerance or self-sensing in magnetic bearings. The present work describes the fundamental connection between ability to actuate and ability to sense in a partially failed magnetic bearing system. This relationship is then exploited to construct a self-sensing scheme which operates in the presence of detectable actuator or amplifier faults. Such an approach is advantageous in fault tolerant systems because it reduces or eliminates the need to address potential independent failure mechanisms in sensors and actuators. Based on a model reference parameter estimation mechanism, the self-sensing scheme is shown to provide acceptable position measurement accuracy and bandwidth under various actuator/amplifier faults which are actuator tolerable. Estimates of increase in noise floor and loss of bandwidth under fault conditions are provided. The issue of estimator convergence under fault conditions is examined in detail with comments on implementation complexity arising from scheduling convergence control on fault state.
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CUI, Yuwei, Aijun LI, and Yang SUN. "Fault diagnosis method for redundancy sensors of electric actuator." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 42, no. 4 (2024): 652–61. http://dx.doi.org/10.1051/jnwpu/20244240652.
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In order to solve the singular fault modes of redundant position sensors in electric actuators, a fault diagnosis method based on Hall sensor analytical model is proposed on the basis of traditional redundancy comparison algorithm. Through the complete self-monitoring design of Hall sensor and the analytical model design, non-similar redundancy signals of sensors are formed. At the same time, a hypothesis testing method based on sliding data window is adopted, and by adjusting the width of the sliding window to meet the indicators of false alarm rate and missed alarm rate, a fault diagnosis algorithm for position sensors is formed as a whole. Finally, an algorithm validation was conducted for the 2∶2 singular fault mode of the position sensor. The results showed that the fault diagnosis algorithm based on the Hall sensor model can effectively solve the problems caused by 2∶2 singular faults, greatly improve the availability of sensor signals, and ensure the control accuracy and fault tolerance of the servo closed-loop.
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Segovia, Pau, Joaquim Blesa, Klaudia Horváth, et al. "Modeling and fault diagnosis of flat inland navigation canals." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 232, no. 6 (2018): 761–71. http://dx.doi.org/10.1177/0959651818773187.
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This article regards the development of an analytical redundancy-based approach for detecting and isolating both sensor and actuator faults in flat inland navigation canals. Inland navigation networks are principally used for transport and are composed of many canalized natural rivers and artificial canals characterized by no slope. These canals are strongly affected by resonance phenomena, which can create waves such that the navigation condition might not be guaranteed. It is, therefore, required to ensure dealing with fault-free measured data and actuators. The proposed approach is based on the integrator delay zero model of the flat inland navigation canal. The proposed method is tested by considering the Cuinchy–Fontinettes navigation reach (in the north of France) to detect and isolate the occurrence of faults in the Cuinchy and Fontinettes level sensors and in the Cuinchy gate.
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Okada, Kenji Fabiano Ávila, Aniel Silva Morais, Laura Ribeiro, et al. "Fault-Tolerant Control for Quadcopters Under Actuator and Sensor Faults." Sensors 24, no. 22 (2024): 7299. http://dx.doi.org/10.3390/s24227299.
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Fault detection and diagnosis (FDD) methods and fault-tolerant control (FTC) have been the focus of intensive research across various fields to ensure safe operation, reduce costs, and optimize maintenance tasks. Unmanned aerial vehicles (UAVs), particularly quadcopters or quadrotors, are often prone to faults in sensors and actuators due to their complex dynamics and exposure to various external uncertainties. In this context, this work implements different FDD approaches based on the Kalman filter (KF) for fault estimation to achieve FTC of the quadcopter, considering different faults with nonlinear behaviors and the possibility of simultaneous occurrences in actuators and sensors. Three KF approaches are considered in the analysis: linear KF, extended KF (EKF), and unscented KF (UKF), along with three-stage and adaptive variations of the KF. FDD methods, especially the adaptive filter, could enhance fault estimation performance in the scenarios considered. This led to a significant improvement in the safety and reliability of the quadcopter through the FTC architecture, as the system, which previously became unstable in the presence of faults, could maintain stable operation when subjected to uncertainties.
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Fourlas, George K., and George C. Karras. "A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles." Machines 9, no. 9 (2021): 197. http://dx.doi.org/10.3390/machines9090197.
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The continuous evolution of modern technology has led to the creation of increasingly complex and advanced systems. This has been also reflected in the technology of Unmanned Aerial Vehicles (UAVs), where the growing demand for more reliable performance necessitates the development of sophisticated techniques that provide fault diagnosis and fault tolerance in a timely and accurate manner. Typically, a UAV consists of three types of subsystems: actuators, main structure and sensors. Therefore, a fault-monitoring system must be specifically designed to supervise and debug each of these subsystems, so that any faults can be addressed before they lead to disastrous consequences. In this survey article, we provide a detailed overview of recent advances and studies regarding fault diagnosis, Fault-Tolerant Control (FTC) and anomaly detection for UAVs. Concerning fault diagnosis, our interest is mainly focused on sensors and actuators, as these subsystems are mostly prone to faults, while their healthy operation usually ensures the smooth and reliable performance of the aerial vehicle.
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Rahnavard, Mostafa, Moosa Ayati, and Mohammad Reza Hairi Yazdi. "Robust actuator and sensor fault reconstruction of wind turbine using modified sliding mode observer." Transactions of the Institute of Measurement and Control 41, no. 6 (2018): 1504–18. http://dx.doi.org/10.1177/0142331218754620.
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This paper proposes a robust fault diagnosis scheme based on modified sliding mode observer, which reconstructs wind turbine hydraulic pitch actuator faults as well as simultaneous sensor faults. The wind turbine under consideration is a 4.8 MW benchmark model developed by Aalborg University and kk-electronic a/s. Rotor rotational speed, generator rotational speed, blade pitch angle and generator torque have different order of magnitudes. Since the dedicated sensors experience faults with quite different values, simultaneous fault reconstruction of these sensors is a challenging task. To address this challenge, some modifications are applied to the classic sliding mode observer to realize simultaneous fault estimation. The modifications are mainly suggested to the discontinuous injection switching term as the nonlinear part of observer. The proposed fault diagnosis scheme does not require know the exact value of nonlinear aerodynamic torque and is robust to disturbance/modelling uncertainties. The aerodynamic torque mapping, represented as a two-dimensional look up table in the benchmark model, is estimated by an analytical expression. The pitch actuator low pressure faults are identified using some fault indicators. By filtering the outputs and defining an augmented state vector, the sensor faults are converted to actuator faults. Several fault scenarios, including the pitch actuator low pressure faults and simultaneous sensor faults, are simulated in the wind turbine benchmark in the presence of measurement noises. Simulation results show that the modified observer immediately and faithfully estimates the actuator faults as well as simultaneous sensor faults with different order of magnitudes.
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Qu, Chunxu, Linsheng Huo, and Hongnan Li. "Fault Tolerant Control for Civil Structures Based on LMI Approach." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/762385.
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The control system may lose the performance to suppress the structural vibration due to the faults in sensors or actuators. This paper designs the filter to perform the fault detection and isolation (FDI) and then reforms the control strategy to achieve the fault tolerant control (FTC). The dynamic equation of the structure with active mass damper (AMD) is first formulated. Then, an estimated system is built to transform the FDI filter design problem to the static gain optimization problem. The gain is designed to minimize the gap between the estimated system and the practical system, which can be calculated by linear matrix inequality (LMI) approach. The FDI filter is finally used to isolate the sensor faults and reform the FTC strategy. The efficiency of FDI and FTC is validated by the numerical simulation of a three-story structure with AMD system with the consideration of sensor faults. The results show that the proposed FDI filter can detect the sensor faults and FTC controller can effectively tolerate the faults and suppress the structural vibration.
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Asensio, Eduardo Maximiliano, Ken King Man Siu, Juan Carlos Astrada, Federico Martín Serra, and Cristian Hernán De Angelo. "Comprehensive Analysis and Reconstruction of Sensor Faults in Interleaved Buck Converters Using Sliding Mode Observers." Electronics 13, no. 21 (2024): 4202. http://dx.doi.org/10.3390/electronics13214202.
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This paper presents a fault signal reconstruction method for current sensors in an interleaved buck DC–DC converter, utilizing a sliding mode observer (SMO). A filter bank is used to design the observer within an extended-order system, effectively treating sensor faults as actuator faults, which enables precise estimation of the fault signal. Thus, the proposed approach allows for the identification of the faulty sensor and supports the implementation of fault-tolerant strategies. The paper provides an in-depth analysis of current sensor faults, verifies their impact on current balancing control, and demonstrates the challenge of achieving error-free current estimation in one phase using observers. A comprehensive set of simulation results is carried out, validating the method’s effectiveness and showing a strong correlation with theoretical principles.
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Domínguez, Carlos, and Claudio Urrea. "Intelligent Fault-Tolerant Control of Delta Robots: A Hybrid Optimization Approach for Enhanced Trajectory Tracking." Sensors 25, no. 6 (2025): 1940. https://doi.org/10.3390/s25061940.
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The kinematic complexity and multi-actuator dependence of Delta-type manipulators render them vulnerable to performance degradation from faults. This study presents a novel approach to Active Fault-Tolerant Control (AFTC) for Delta-type parallel robots, integrating an advanced fault diagnosis system with a robust control strategy. In the first stage, a fault diagnosis system is developed, leveraging a hybrid feature extraction algorithm that combines Wavelet Scattering Networks (WSNs), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Meta-Learning (ML). This system effectively identifies and classifies faults affecting single actuators, sensors, and multiple components under real-time conditions. The proposed AFTC approach employs a hybrid optimization framework that integrates Genetic Algorithms and Gradient Descent to reconfigure a Type-2 fuzzy controller. Results show that the methodology achieves perfect fault diagnosis accuracy across four classifiers and enhances robot performance by reducing critical degradation to moderate levels under multiple faults. These findings validate the robustness and efficiency of the proposed fault-tolerant control strategy, highlighting its potential for enhancing trajectory tracking accuracy in complex robotic systems under adverse conditions.
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Moussaoui, L., S. Aouaouda, and R. Rouaibia. "Fault tolerant control of a permanent magnet synchronous machine using multiple constraints Takagi-Sugeno approach." Electrical Engineering & Electromechanics, no. 6 (November 6, 2022): 22–27. http://dx.doi.org/10.20998/2074-272x.2022.6.04.
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Introduction. Fault diagnosis, and fault tolerant control issues are becoming very important to ensure a good supervision of systems and guarantee the safety of human operators and equipments even if system complexity increases. Problem. In fact, the presence of faults in actuators, sensors and processes can lead to system performance degradation, system breakdown, economic loss, and even disastrous situations. Furthermore, Actuator saturation or control input saturation is probably the most usual nonlinearity encountered in control engineering because of the physical impossibility of applying unlimited control signals and/or safety constraints. Purpose. This article is dedicated to the problem of fault tolerant control for constrained nonlinear systems described by a Takagi-Sugeno model. One of the interests of this type of models is the possibility of extend some tools and methods from linear system case to the nonlinear one. The novelty of the work consists in developing a fault tolerant control algorithm for a nonlinear Permanent Magnet Synchronous Machine model using an observer based state-feedback control technique in order to enhance fault and state estimation despite actuator saturation and system disturbances. Methods. Indeed a sensor fault detection observer based residual generator is synthesized with a guaranteed L2 performance to attenuate the external disturbances effect from one side and to maximize the residual sensitivity to faults from the other side. Based on Lyapunov function, design conditions are formulated in terms of Linear Matrix Inequalities to ensure stability of the global system. Practical value. A detailed study concerning nonlinear permanent magnet synchronous machine model, which is consolidated by simulation results, is conducted to show the used algorithm’s effectiveness guarantying fault estimation and reconfiguration of the control law to maintain stable performance even in the presence of actuator faults, external perturbation and the phenomenon of actuator saturation.
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Bae, Jangsik, Meonghun Lee, and Changsun Shin. "A Data-Based Fault-Detection Model for Wireless Sensor Networks." Sustainability 11, no. 21 (2019): 6171. http://dx.doi.org/10.3390/su11216171.
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With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in this study to analyze data of sensor nodes and determine faults, to prevent the corresponding nodes from transmitting data; thus, minimizing damage to the network. A cloud-based “farm as a service” optimized for smart farms was implemented as an example, and resource management of sensors and actuators was provided using the oneM2M common platform. The effectiveness of the proposed fault-detection model was verified on an integrated management platform based on the Internet of Things by collecting and analyzing data. The results confirm that when a faulty sensor node is not separated from the network, unnecessary data transmission of other sensor nodes occurs due to continuous abnormal data transmission; thus, increasing energy consumption and reducing the network lifetime.
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Gienger, Andreas, Andreas Ostertag, Michael Böhm, Bernd Bertsche, Oliver Sawodny, and Cristina Tarín. "Data-based Distributed Fault Diagnosis for Adaptive Structures using Convolutional Neural Networks." Unmanned Systems 08, no. 03 (2020): 221–28. http://dx.doi.org/10.1142/s2301385020500156.
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Adaptive structures are able to react to environmental impacts and have become a promising approach in civil engineering to improve the load-bearing behavior of buildings. Since reliability and safety of building structures are major concerns, the detection and isolation of faults are essential. In this work, the data-based distributed fault diagnosis of sensor and actuator faults in an adaptive high-rise truss structure is investigated and compared to a centralized approach. The decomposition of the different subsystems is given by the hardware layout of the different sensor systems and actuators. The mechanical structure is modeled and extended by dynamic sensor and actuator models containing different faults. Based on the simulation model, different fault scenarios are generated and used for training a convolutional neural network with dropout regularization. It is shown that the distributed approach needs less training data and yields better classification results than the centralized approach due to a significant reduction of the complexity and dimensionality.
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Yin, Jian Bo, Qi Chen, Yun Bo Ma, and Yv Cai Zhao. "Study on Automated Manual Transmission Fault Diagnosis and Tolerant Control." Applied Mechanics and Materials 697 (November 2014): 344–49. http://dx.doi.org/10.4028/www.scientific.net/amm.697.344.
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This paper discuss the possible fault about AMT. By using the information redundancy between those parts, the faults of sensors and actuators can be found. In addition, the corresponding tolerant is put forward. Based on the fault diagnosis method, Matlab/Simulink mathematical model of engine, clutch and transmission is built. The simulation results show that the model can satisfy the requirement of fault diagnosis, and has certain tolerances.
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Torabi, Narges, H. Burak Gunay, and William O’Brien. "A review of common human errors in design, installation, and operation of multiple-zone VAV AHU systems." Journal of Physics: Conference Series 2042, no. 1 (2021): 012130. http://dx.doi.org/10.1088/1742-6596/2042/1/012130.
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Abstract Faults in air-based heating, ventilation, and air conditioning (HVAC) systems lead to energy waste and discomfort. While the emphasis of fault detection and diagnostic (FDD) research has been on hard faults in actuators, sensors, and equipment, faults arising from human errors account for a significant portion of faults occurring in HVAC systems. In this paper, human errors occurring in air handling units (AHUs) and variable air volume (VAV) thermal zones during design, construction, and operation phases are identified through a review of the literature. Then, the faults are divided into six main categories. Based on case studies investigating these faults, the impact of each fault category on occupant comfort, energy consumption, and equipment life is discussed. The authors provide recommendations to minimize human errors in AHUs and VAV zones throughout the building life cycle.
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Qin, Wei-Li, Wen-Jin Zhang, and Chen Lu. "A Method for Aileron Actuator Fault Diagnosis Based on PCA and PGC-SVM." Shock and Vibration 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/4807250.
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Aileron actuators are pivotal components for aircraft flight control system. Thus, the fault diagnosis of aileron actuators is vital in the enhancement of the reliability and fault tolerant capability. This paper presents an aileron actuator fault diagnosis approach combining principal component analysis (PCA), grid search (GS), 10-fold cross validation (CV), and one-versus-one support vector machine (SVM). This method is referred to as PGC-SVM and utilizes the direct drive valve input, force motor current, and displacement feedback signal to realize fault detection and location. First, several common faults of aileron actuators, which include force motor coil break, sensor coil break, cylinder leakage, and amplifier gain reduction, are extracted from the fault quadrantal diagram; the corresponding fault mechanisms are analyzed. Second, the data feature extraction is performed with dimension reduction using PCA. Finally, the GS and CV algorithms are employed to train a one-versus-one SVM for fault classification, thus obtaining the optimal model parameters and assuring the generalization of the trained SVM, respectively. To verify the effectiveness of the proposed approach, four types of faults are introduced into the simulation model established by AMESim and Simulink. The results demonstrate its desirable diagnostic performance which outperforms that of the traditional SVM by comparison.
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Tortora, Giacomo, Basil Kouvaritakis, and David W. Clarke. "OPTIMAL ACCOMMODATION OF FAULTS IN SENSORS AND ACTUATORS." IFAC Proceedings Volumes 35, no. 1 (2002): 83–88. http://dx.doi.org/10.3182/20020721-6-es-1901.00745.
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Zhang, Qinghua. "Actuator Fault Diagnosis with Robustness to Sensor Distortion." Journal of Control Science and Engineering 2008 (2008): 1–7. http://dx.doi.org/10.1155/2008/723292.
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Actuator fault diagnosis is often studied under strong assumptions on available sensors. Typically, it is assumed that the sensors are either fault free or sufficiently redundant. The purpose of this paper is to present a new method foractuatorfault diagnosis which is robust tosensordistortion. It does not require sensor redundancy to compensate sensor distortion. The essential assumption is that sensor distortions are strictly monotonous. Despite the nonlinear and unknown nature of distortions, such sensors still provide useful information for fault diagnosis. The robustness of the presented diagnosis method is analyzed, as well as its ability to detect actuator faults. A numerical example is provided to illustrate its efficiency.
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Saci, Abdelmoumen, Mohamed Nadour, Lakhmissi Cherroun, et al. "Condition Monitoring Using Digital Fault-Detection Approach for Pitch System in Wind Turbines." Energies 17, no. 16 (2024): 4016. http://dx.doi.org/10.3390/en17164016.
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The monitoring of wind turbine (WT) systems allows operators to maximize their performance, consequently minimizing untimely shutdowns and related hazard situations while maximizing their efficiency. Indeed, the rational monitoring of WT ensures the identification of the main sources of risks at a proper time, such as internal or external failures, hence leading to an increase in their prevention by limiting the faults’ occurrence regarding the different components of wind turbines, achieving production objectives. In this context, the present paper develops a practical monitoring approach using a numerical fault-detection process for the pitch system based on a benchmark wind turbine (WT) model with the main aim of improving safety and security performance. Therefore, the proposed fault-diagnosis procedure deals with eventual faults occurring in the actuators and sensors of the pitch system. In this proposed approach, a simple, logical process is used to generate the correct residuals as fault information based on the redundancy in the actuators and sensors of the pitch sub-systems. The obtained results demonstrate the effectiveness of this proposed process for ensuring the tasks of the fault diagnosis and condition monitoring of the WT systems, and it can be a promising approach for avoiding major damage in such systems, leading to their operational stability and improved reliability and availability.
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Soliman, Hisham M., Ehab H. E. Bayoumi, Farag A. El-Sheikhi, and Michele De Santis. "Decentralized Sensor Fault-Tolerant Control of DC Microgrids Using the Attracting Ellipsoid Method." Sensors 23, no. 16 (2023): 7160. http://dx.doi.org/10.3390/s23167160.
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System stability deterioration in microgrids commonly occurs due to unpredictable faults and equipment malfunctions. Recently, robust control techniques have been used in microgrid systems to address these difficulties. In this paper, for DC-islanded microgrids that have sensors faults, a new passive fault-tolerant control strategy is developed. The suggested approach can be used to maintain system stability in the presence of flaws, such as faulty actuators and sensors, as well as component failures. The suggested control is effective when the fault is never recognized (or when the fault is not being precisely known, and some ambiguity in the fault may be interpreted as uncertainty in the system’s dynamics following the fault). The design is built around a derived sufficient condition in the context of linear matrix inequalities (LMIs) and the attractive ellipsoid technique. The ellipsoidal stabilization idea is to bring the state trajectories into a small region including the origin (an ellipsoid with minimum volume) and the trajectories will not leave the ellipsoid for the future time. Finally, computational studies on a DC microgrid system are carried out to assess the effectiveness of the proposed fault-tolerant control approach. When compared with previous studies, the simulation results demonstrate that the proposed control technique can significantly enhance the reliability and efficiency of DC microgrid systems.
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Ma, Chong, and Haozheng Zhang. "A data-driven UUV decision-making test strategy design method." Journal of Physics: Conference Series 2674, no. 1 (2023): 012011. http://dx.doi.org/10.1088/1742-6596/2674/1/012011.
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Abstract Based on the analysis of UUV control architecture and workflow, a data-driven test strategy design method was proposed to realize comprehensive online inspection of UUV, aiming at the requirements of full-process and full-function commissioning, system-level fault testing, single-step action of the actuator, software boundary testing, emergency control and fault-tolerant function verification of a certain UUV. At the same time, three types of typical faults of software, sensors and actuators are identified, and pseudo-random fault codes are injected into UUV control flow or information flow to realize the test verification of action continuity, function matching and control effectiveness under UUV operating conditions. The engineering application proves that the strategy has clear logic and comprehensive functional coverage, laying the foundation for improving UUV reliability and sailing safety.
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Hamayun, Mirza Tariq, Christopher Edwards, Halim Alwi, and Abdulrahman Bajodah. "A Fault Tolerant Direct Control Allocation Scheme with Integral Sliding Modes." International Journal of Applied Mathematics and Computer Science 25, no. 1 (2015): 93–102. http://dx.doi.org/10.1515/amcs-2015-0007.
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Abstract In this paper, integral sliding mode control ideas are combined with direct control allocation in order to create a fault tolerant control scheme. Traditional integral sliding mode control can directly handle actuator faults; however, it cannot do so with actuator failures. Therefore, a mechanism needs to be adopted to distribute the control effort amongst the remaining functioning actuators in cases of faults or failures, so that an acceptable level of closed-loop performance can be retained. This paper considers the possibility of introducing fault tolerance even if fault or failure information is not provided to the control strategy. To demonstrate the efficacy of the proposed scheme, a high fidelity nonlinear model of a large civil aircraft is considered in the simulations in the presence of wind, gusts and sensor noise.
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L., Moussaoui, Aouaouda S., and Rouaibia R. "Fault tolerant control of a permanent magnet synchronous machine using multiple constraints Takagi-Sugeno approach." Electrical Engineering & Electromechanics, no. 6 (November 6, 2022): 22–27. https://doi.org/10.20998/2074-272X.2022.6.04.
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<strong><em>Introduction. </em></strong><em>Fault diagnosis, and fault tolerant control issues are becoming very important to ensure a good supervision of systems and guarantee the safety of human operators and equipments even if system complexity increases<strong>. Problem.</strong> In fact, the presence of faults in actuators, sensors and processes can lead to system performance degradation, system breakdown, economic loss, and even disastrous situations. Furthermore, Actuator saturation or control input saturation is probably the most usual nonlinearity encountered in control engineering because of the physical impossibility of applying unlimited control signals and/or safety constraints.<strong> Purpose. </strong>This article is dedicated to the problem of fault tolerant control for constrained nonlinear systems described by a Takagi-Sugeno model. One of the interests of this type of models is the possibility of extend some tools and methods from linear system case to the nonlinear one.<strong> The novelty </strong>of the work consists in developing a fault tolerant control algorithm for a nonlinear Permanent Magnet Synchronous Machine model using an observer based state-feedback control technique in order to enhance fault and state estimation despite actuator saturation and system disturbances. <strong>Methods.</strong> Indeed a sensor fault detection observer based residual generator is synthesized with a guaranteed L<sub>2 </sub>performance to attenuate the external disturbances effect from one side and to maximize the residual sensitivity to faults from the other side. Based on Lyapunov function, design conditions are formulated in terms of Linear Matrix Inequalities to ensure stability of the global system.<strong> Practical value. </strong>A detailed study concerning nonlinear permanent magnet synchronous machine model, which is consolidated by simulation results, is conducted to show the used algorithm’s effectiveness guarantying fault estimation and reconfiguration of the control law to maintain stable performance even in the presence of actuator faults, external perturbation and the phenomenon of actuator saturation.</em>
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Kobayashi, Takahisa, and Donald L. Simon. "Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics." Journal of Engineering for Gas Turbines and Power 127, no. 3 (2005): 497–504. http://dx.doi.org/10.1115/1.1850505.
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In this paper, an approach for in-flight fault detection and isolation (FDI) of aircraft engine sensors based on a bank of Kalman filters is developed. This approach utilizes multiple Kalman filters, each of which is designed based on a specific fault hypothesis. When the propulsion system experiences a fault, only one Kalman filter with the correct hypothesis is able to maintain the nominal estimation performance. Based on this knowledge, the isolation of faults is achieved. Since the propulsion system may experience component and actuator faults as well, a sensor FDI system must be robust in terms of avoiding misclassifications of any anomalies. The proposed approach utilizes a bank of m+1 Kalman filters where m is the number of sensors being monitored. One Kalman filter is used for the detection of component and actuator faults while each of the other m filters detects a fault in a specific sensor. With this setup, the overall robustness of the sensor FDI system to anomalies is enhanced. Moreover, numerous component fault events can be accounted for by the FDI system. The sensor FDI system is applied to a nonlinear simulation of a commercial aircraft gas turbine engine, and its performance is evaluated at multiple power settings at a cruise operating point using various fault scenarios.
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Zhang, Changfan, Huijun Liao, Xiangfei Li, Jian Sun, and Jing He. "Fault Reconstruction Based on Sliding Mode Observer for Current Sensors of PMSM." Journal of Sensors 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/9307560.
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This paper deals with a method of phase current sensor fault reconstruction for permanent magnet synchronous motor (PMSM) drives. A new state variable is introduced so that an augmented system can be constructed to treat PMSM sensor faults as actuator faults. This method uses the PMSM two-phase stationary reference frame fault model and a sliding mode variable structure observer to reconstruct fault signals. A logic algorithm is built to isolate and identify the faulty sensor for a stator phase current fault after reconstructing the two-phase stationary reference frame fault signals, which allows the phase fault signals to be reconstructed. Simulation results are presented to illustrate the functionality of the theoretical developments.
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Encalada-Dávila, Á., C. Tutivén, B. Puruncajas, and Y. Vidal. "Wind Turbine Multi-Fault Detection based on SCADA Data via an AutoEncoder." Renewable Energy and Power Quality Journal 19 (September 2021): 487–92. http://dx.doi.org/10.24084/repqj19.325.
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Nowadays, wind turbine fault detection strategies are settled as a meaningful pipeline to achieve required levels of efficiency, availability, and reliability, considering there is an increasing installation of this kind of machinery, both in onshore and offshore configuration. In this work, it has been applied a strategy that makes use of SCADA data with an increased sampling rate. The employed wind turbine in this study is based on an advanced benchmark, established by the National Renewable Energy Laboratory (NREL) of USA. Different types of faults on several actuators and sensed by certain installed sensors have been studied. The proposed strategy is based on a normality model by means of an autoencoder. As of this, faulty data are used for testing from which prediction errors were computed to detect if those raise a fault alert according to a defined metric which establishes a threshold on which a wind turbine works securely. The obtained results determine that the proposed strategy is successful since the model detects the considered three types of faults. Finally, even when prediction errors are small, the model is able to detect the faults without problems.
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Filaretov, Vladimir, Alexander Zuev, Alexander Procenko, and Sergey Melman. "Fault Detection of Actuators of Robot Manipulator by Vision System." Applied Mechanics and Materials 865 (June 2017): 457–62. http://dx.doi.org/10.4028/www.scientific.net/amm.865.457.
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This paper considers synthesis method of fault detection system for actuators of robot manipulators based on using of signals fusion from stereo camera, angles sensors of joints and desired values of joint variables. The vision system is used for determining the position of three markers rigidly connected with working tool in the coordinate system associated with the manipulator. The advantage of proposed fault detection system is the simplicity of implementation and precision of detection of typical faults without knowledge about non-linear dynamic of robot and actuators. The results of mathematical simulation on the example of the PUMA-type manipulator using its kinematic model, position and orientation data of markers placed on working tool of manipulator, obtained from vision system fully confirm the efficiency of the proposed fault detection system.
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Talebi, Nasser, Mohammad Ali Sadrnia, and Ahmad Darabi. "Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks." Computational Intelligence and Neuroscience 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/580972.
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Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.
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Sanchez, Oscar D., Gabriel Martinez-Soltero, Jesus G. Alvarez, and Alma Y. Alanis. "Real-Time Neural Classifiers for Sensor and Actuator Faults in Three-Phase Induction Motors." Machines 10, no. 12 (2022): 1198. http://dx.doi.org/10.3390/machines10121198.
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The main steps involved in a fault-tolerant control (FTC) scheme are the detection of failures, isolation and reconfiguration of control. Fault detection and isolation (FDI) is a topic of interest due to its importance for the controller, since it provides the necessary information to adjust and mitigate the effects of the fault. Generally, the most common failures occur in the actuator or in sensors, so this article proposes a novel model-free scheme for the detection and isolation of sensor and actuator faults of induction motors (IM). The proposed methodology performs the task of detecting and isolating faults over data streams just after the occurrence of the failure of an induction motor (IM), by the occurrence of either disconnection, degradation, failure, or connection damage. Our approach proposes deep neural networks that do not need a nominal model or generate residuals for fault detection, which makes it a useful tool. In addition, the fault-isolation approach is carried out by classifiers that differentiate characteristics independently of the other classifiers. The long short-term memory (LSTM) neural network, bidirectional LSTM, multilayer perceptron and convolutional neural network are used for this task. The proposed sensors’ and actuator’s fault detection and isolation scheme is simple. It can be applied to various problems involving fault detection and isolation schemes. The results show that deep neural networks are a powerful and versatile tool for fault detection and isolation over data streams.
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Xiao, Lingfei, Yanbin Du, Jixiang Hu, and Bin Jiang. "Sliding Mode Fault Tolerant Control with Adaptive Diagnosis for Aircraft Engines." International Journal of Turbo & Jet-Engines 35, no. 1 (2018): 49–57. http://dx.doi.org/10.1515/tjj-2016-0023.
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AbstractIn this paper, a novel sliding mode fault tolerant control method is presented for aircraft engine systems with uncertainties and disturbances on the basis of adaptive diagnostic observer. By taking both sensors faults and actuators faults into account, the general model of aircraft engine control systems which is subjected to uncertainties and disturbances, is considered. Then, the corresponding augmented dynamic model is established in order to facilitate the fault diagnosis and fault tolerant controller design. Next, a suitable detection observer is designed to detect the faults effectively. Through creating an adaptive diagnostic observer and based on sliding mode strategy, the sliding mode fault tolerant controller is constructed. Robust stabilization is discussed and the closed-loop system can be stabilized robustly. It is also proven that the adaptive diagnostic observer output errors and the estimations of faults converge to a set exponentially, and the converge rate greater than some value which can be adjusted by choosing designable parameters properly. The simulation on a twin-shaft aircraft engine verifies the applicability of the proposed fault tolerant control method.
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Chouiref, Houda, Boumedyen Boussaid, Mohamed Naceur Abdelkrim, Vicenç Puig, and Christophe Aubrun. "Integrated FDI/FTC approach for wind turbines using a LPV interval predictor subspace approach and virtual sensors/actuators." Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 235, no. 6 (2021): 1527–43. http://dx.doi.org/10.1177/09576509211002080.
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In order to keep wind turbines connected and in operation at all times despite the occurrence of some faults, advanced fault detection and accommodation schemes are required. To achieve this goal, this paper proposes to use the Linear Parameter Varying approach to design an Active Fault Tolerant Control for wind turbines. This Active Fault Tolerant Control is integrated with a Fault Detection and Isolation approach. Fault detection is based on a Linear Parameter Varying interval predictor approach while fault isolation is based on analysing the residual fault signatures. To include fault-tolerance in the control system (already available in the considered wind turbine case study based on the well known SAFEPROCESS benchmark), the information of the Fault Detection and Isolation approach block is exploited and it is used in the implementation of a virtual actuator and sensor scheme. The proposed Active Fault Tolerant Control is evaluated using fault scenarios which are proposed in the wind turbine benchmark to assess its performance. Results show the effectiveness of the proposed Active Fault Tolerant Control approach in faulty situation.