Статті в журналах: "Robust fault diagnosis"

1

Wang, Ban, Peng Huang, and Wei Zhang. "A Robust Fault-Tolerant Control for Quadrotor Helicopters against Sensor Faults and External Disturbances." Complexity 2021 (March 19, 2021): 1–13. http://dx.doi.org/10.1155/2021/6672812.

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This paper presents an active fault-tolerant control strategy for quadrotor helicopters to simultaneously accommodate sensor faults and external disturbances. Unlike most of the existing fault diagnosis and fault-tolerant control schemes for quadrotor helicopters, the proposed fault diagnosis scheme is able to estimate sensor faults while eliminating the effect of external disturbances. Moreover, the proposed fault-tolerant control scheme is capable to eliminate the adverse effect of external disturbances as well by designing a disturbance observer to effectively estimate the unknown external disturbances and integrating with the designed integral sliding-mode controller. In this case, the continuous operation of the quadrotor helicopter is ensured while avoiding the unexpected control chattering. In addition, the stability of the closed-loop system is theoretically proved. Finally, the effectiveness and advantages of the proposed scheme are validated and demonstrated through comparative numerical simulations of the quadrotor helicopter under different faulty and uncertain scenarios.

2

Li, Jian, Xinxin Guo, and Bo Li. "Robust Fault Diagnosis and Adaptive Parameter Identification for Single Phase Transformerless Inverters." Mathematical Problems in Engineering 2018 (August 13, 2018): 1–11. http://dx.doi.org/10.1155/2018/3025838.

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The paper presents the theoretical analysis and simulation verification of robust fault diagnosis and adaptive parameter identification for single phase transformerless inverters. The fault diagnosis is composed of two parts, fault detection and fault identification. In the fault detection part, a Luenberger observer is designed to realize the detection of faults. Then, we apply a bank of observers to identify the location of faults. Meanwhile, the fault identification observers based estimation along with a gradient descent algorithm are also used in the parameter identification to estimate the actual values of components in a single phase transformerless inverter. Not only we develop the design methodology for the robust fault diagnosis and adaptive parameter identifier but also we present simulation results. The simulation results show the effectiveness of fault diagnosis and the accurate tracking of changes in component parameters for a wide range.

3

Nguyen, Ngoc Phi, and Sung Kyung Hong. "Active Fault-Tolerant Control of a Quadcopter against Time-Varying Actuator Faults and Saturations Using Sliding Mode Backstepping Approach." Applied Sciences 9, no. 19 (September 25, 2019): 4010. http://dx.doi.org/10.3390/app9194010.

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Fault-tolerant control is becoming an interesting topic because of its reliability and safety. This paper reports an active fault-tolerant control method for a quadcopter unmanned aerial vehicle (UAV) to handle actuator faults, disturbances, and input constraints. A robust fault diagnosis based on the H ∞ scheme was designed to estimate the magnitude of a time-varying fault in the presence of disturbances with unknown upper bounds. Once the fault estimation was complete, a fault-tolerant control scheme was proposed for the attitude system, using adaptive sliding mode backstepping control to accommodate the actuator faults, despite actuator saturation limitation and disturbances. The Lyapunov theory was applied to prove the robustness and stability of the closed-loop system under faulty operation. Simulation results show the effectiveness of the fault diagnosis scheme and proposed controller for handling actuator faults.

4

Zhao, Chunheng, Yi Li, Matthew Wessner, Chinmay Rathod, and Pierluigi Pisu. "Support-Vector Machine Approach for Robust Fault Diagnosis of Electric Vehicle Permanent Magnet Synchronous Motor." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 10. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1291.

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Permanent magnet synchronous motor (PMSM) is a leading technology for electric vehicles (EVs) and other high-performance industrial applications. These challenging applications demand robust fault diagnosis schemes, but conventional strategies based on models, system knowledge, and signal transformation have limitations that degrade the agility of diagnosing faults. These methods require extremely detailed design and consideration to remain robust against noise and disturbances in the actual application. Recent advancements in artificial intelligence and machine learning have proven to be promising next-generation solutions for fault diagnosis. In this paper, a support-vector machine (SVM) utilizing sparse representation is developed to perform sensor fault diagnosis of a PMSM. A simulation model of the pertinent PMSM drive system for automotive applications is used to generate a set of labelled training example sets that the SVM uses to determine margins between normal and faulty operating conditions. The PMSM model includes input as a torque reference profile and disturbance as a constant road grade, against both of which faults must be detectable. Even with limited training, the SVM classifier developed in this paper is capable of diagnosing faults with a high degree of accuracy, suggesting that such methods are feasible for the demanding fault diagnosis challenge in PMSM.

5

Manikandan, V., and N. Devarajan. "SBT Approach towards Analog Electronic Circuit Fault Diagnosis." Active and Passive Electronic Components 2007 (2007): 1–12. http://dx.doi.org/10.1155/2007/59856.

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An approach for the fault diagnosis of single and multiple faults in linear analog electronic circuits is proposed in this paper. The simulation-before-test (SBT) diagnosis approach proposed in this write up basically consists of obtaining the frequency response of fault free/faulty circuit. The peak frequency and the peak amplitude from the error response are observed and processed suitably to extract distinct signatures for faulty and nonfaulty conditions under maximum tolerance conditions for other network components. The artificial neural network classifiers are then used for the classification of fault. Networks of reasonable dimensions are shown to be capable of robust diagnosis of analog circuits including effects due to tolerances. This is a unique contribution of this paper. Fault computation time is drastically reduced from the traditional analysis techniques. This results in a direct dollar savings at test time. A comparison of the proposed work with the previous works which also employ preprocessing techniques, reveals that our algorithm performs significantly better in fault diagnosis of analog circuits due to our proposed preprocessing techniques.

6

Rashid, Umair, Muhammad Asim Abbasi, Abdul Qayyum Khan, Muhammad Irfan, Muhammad Abid, and Grzegorz Nowakowski. "Robust Data-Driven Design for Fault Diagnosis of Industrial Drives." Electronics 11, no. 23 (November 23, 2022): 3858. http://dx.doi.org/10.3390/electronics11233858.

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Due to the presence of actuator disturbances and sensor noise, increased false alarm rate and decreased fault detection rate in fault diagnosis systems have become major concerns. Various performance indexes are proposed to deal with such problems with certain limitations. This paper proposes a robust performance-index based fault diagnosis methodology using input–output data. That data is used to construct robust parity space using the subspace identification method and proposed performance index. Generated residual shows enhanced sensitivity towards faults and robustness against unknown disturbances simultaneously. The threshold for residual is designed using the Gaussian likelihood ratio, and the wavelet transformation is used for post-processing. The proposed performance index is further used to develop a fault isolation procedure. To specify the location of the fault, a modified fault isolation scheme based on perfect unknown input decoupling is proposed that makes actuator and sensor residuals robust against disturbances and noise. The proposed detection and isolation scheme is implemented on the induction motor in the experimental setup. The results have shown the percentage fault detection of 98.88%, which is superior among recent research.

7

Xia, Jingping, Bin Jiang, Ke Zhang, and Jinfa Xu. "Robust Fault Diagnosis Design for Linear Multiagent Systems with Incipient Faults." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/436935.

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The design of a robust fault estimation observer is studied for linear multiagent systems subject to incipient faults. By considering the fact that incipient faults are in low-frequency domain, the fault estimation of such faults is proposed for discrete-time multiagent systems based on finite-frequency technique. Moreover, using the decomposition design, an equivalent conclusion is given. Simulation results of a numerical example are presented to demonstrate the effectiveness of the proposed techniques.

8

Pazera, Marcin, and Marcin Witczak. "A Novel Adaptive Sensor Fault Estimation Algorithm in Robust Fault Diagnosis." Sensors 22, no. 24 (December 8, 2022): 9638. http://dx.doi.org/10.3390/s22249638.

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The paper deals with a robust sensor fault estimation by proposing a novel algorithm capable of reconstructing faults occurring in the system. The provided approach relies on calculating the fault estimation adaptively in every discrete time instance. The approach is developed for the systems influenced by unknown measurement and process disturbance. Such an issue has been handled with applying the commonly known H∞ approach. The novelty of the proposed algorithm consists of eliminating a difference between consecutive samples of the fault in an estimation error. This results in a easier way of designing the robust estimator by simplification of the linear matrix inequalities. The final part of the paper is devoted to an illustrative example with implementation to a laboratory two-rotor aerodynamical system.

9

Wahlberg, B. "Robust Frequency Domain Fault Detection/Diagnosis." IFAC Proceedings Volumes 23, no. 8 (August 1990): 373–78. http://dx.doi.org/10.1016/s1474-6670(17)51852-2.

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10

Khan, Sazzed Mahamud, and Abu Hena MD Shatil. "A Robust Fault Diagnosis Scheme using Deep Learning for High Voltage Transmission Line." AIUB Journal of Science and Engineering (AJSE) 21, no. 2 (November 23, 2022): 68–75. http://dx.doi.org/10.53799/ajse.v21i2.204.

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The transmission lines repeatedly face an aggregation of shunt-faults and its impact in the real time system increases the vulnerability, damage in load, and line restoration cost. Fault detection in power transmission lines have become significantly crucial due to a rapid increase in number and length. Any kind of interruption or tripping in transmission lines can result in a massive failure over a large area, which necessitates the need of effective protection. The diagnosis of faults help in detecting and classifying transients that eventually make the protection of transmission lines convenient. In this paper, we propose a deep learning-enabled technique for the detection and classification of transmission line faults. The faulty information are extracted using Discrete Wavelet Transform (DWT) and fed into the multilayer perceptron classification model. The results indicate that the proposed approach is capable of accurately classifying and detecting faults in transmission line with high precision.

11

Van, Mien, Pasquale Franciosa, and Dariusz Ceglarek. "Fault Diagnosis and Fault-Tolerant Control of Uncertain Robot Manipulators Using High-Order Sliding Mode." Mathematical Problems in Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/7926280.

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A robust fault diagnosis and fault-tolerant control (FTC) system for uncertain robot manipulators without joint velocity measurement is presented. The actuator faults and robot manipulator component faults are considered. The proposed scheme is designed via an active fault-tolerant control strategy by combining a fault diagnosis scheme based on a super-twisting third-order sliding mode (STW-TOSM) observer with a robust super-twisting second-order sliding mode (STW-SOSM) controller. Compared to the existing FTC methods, the proposed FTC method can accommodate not only faults but also uncertainties, and it does not require a velocity measurement. In addition, because the proposed scheme is designed based on the high-order sliding mode (HOSM) observer/controller strategy, it exhibits fast convergence, high accuracy, and less chattering. Finally, computer simulation results for a PUMA560 robot are obtained to verify the effectiveness of the proposed strategy.

12

Mustafa, Mohammed Obaid, George Nikolakopoulos, and Thomas Gustafsson. "Faults Classification Scheme for Three Phase Induction Motor." International Journal of System Dynamics Applications 3, no. 1 (January 2014): 1–20. http://dx.doi.org/10.4018/ijsda.2014010101.

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In every kind of industrial application, the operation of fault detection and diagnosis for induction motors is of paramount importance. Fault diagnosis and detection led to minimize the downtime and improves its reliability and availability of the systems. In this article, a fault classification algorithm based on a robust linear discrimination scheme, for the case of a squirrel–cage three phase induction motor, will be presented. The suggested scheme is based on a novel feature extraction mechanism from the measured magnitude and phase of current park's vector pattern. The proposed classification algorithm is applied to detect of two kinds of induction machine faults, which area) broken rotor bar, and b) short circuit in stator winding. The novel feature generation technique is able to transform the problem of fault detection and diagnosis into a simpler space, where direct robust linear discrimination can be applied for solving the classification problem. And thus a clear classification of the healthy and the faulty cases can be robustly performed, by having the optimal hyper plane. This method can separate the feature current classes in a low dimensional subspace. Robust linear discrimination has been one of the most widely used fault detection methods in real-life applications, as this methodology seeks for directions that are efficient for discrimination and at the same time applies a straight-forward implementation. The efficacy of the proposed scheme will be evaluated based on multiple simulation results in different fault types.

13

Qiu, Gen, Fan Wu, Kai Chen, and Li Wang. "A Robust Accuracy Weighted Random Forests Algorithm for IGBTs Fault Diagnosis in PWM Converters without Additional Sensors." Applied Sciences 12, no. 4 (February 17, 2022): 2121. http://dx.doi.org/10.3390/app12042121.

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When an insulated-gate bipolar transistor (IGBT) open-circuit fault occurs, a three-phase pulse-width modulated (PWM) converter can usually keep working, which will lead to system instability and more serious secondary faults. The fault detection and diagnosis of the converter is extremely necessary to improve the reliability of the power supply system. In order to solve the problem of fault misdiagnosis caused by parameters disturbance, this paper proposes a robust accuracy weighted random forests online fault diagnosis model to accurately locate various IGBTs open-circuit faults. Firstly, the fault signal features are preprocessed by using the three-phase current signal and normalization method. Based on the test accuracy of the perturbed out-of-bag data and the multiple converters test data, a robust accuracy weighted random forests algorithm is proposed for extracting a mapping relationship between fault modes and current signal. In order to further improve the fault diagnosis performance, a parameter optimization model is built to optimize hyper-parameters of the proposed method. Finally, comparative simulation and online fault diagnosis experiments are carried out, and the results demonstrate the effectiveness and superiority of the method.

14

Al-Shatri, Ali H., Ahmad Arshad, Oladokun Olagoke, and Bemgba B. Nyakuma. "Unknown input observer design for fault detection and diagnosis in a continuous stirred-tank reactor." E3S Web of Conferences 90 (2019): 02004. http://dx.doi.org/10.1051/e3sconf/20199002004.

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Early and accurate fault detection and diagnosis (FDD) minimises downtime, increases the safety and reliability of plant operation, and reduces manufacturing costs. This paper presents a robust FDD strategy for a nonlinear system using a bank of unknown input observers (UIO). The approach is based on structure residual generation that provides not only decoupling of faults from model uncertainties and unknown input disturbance but also decoupling the effect of a fault from the effects of other faults. The generated residual was evaluated through the statistical threshold to avoid fault missing or false alarm. The performance of the robust FDD scheme was assessed by some sensor fault scenarios created in a continuous stirred-tank reactor (CSTR). The simulation result showed the effectiveness of the proposed approach.

15

Vemuri, Arun T., and Marios M. Polycarpou. "A methodology for fault diagnosis in robotic systems using neural networks." Robotica 22, no. 4 (August 2004): 419–38. http://dx.doi.org/10.1017/s0263574703005204.

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Fault diagnosis plays an important role in the operation of modern robotic systems. A number of researchers have proposed fault diagnosis architectures for robotic manipulators using the model-based analytical redundancy approach. One of the key issues in the design of such fault diagnosis schemes is the effect of modeling uncertainties on their performance. This paper investigates the problem of fault diagnosis in rigid-link robotic manipulators with modeling uncertainties. A learning architecture with sigmoidal neural networks is used to monitor the robotic system for off-nominal behavior due to faults. The robustness, sensitivity, missed detection and stability properties of the fault diagnosis scheme are rigorously established. Simulation examples are presented to illustrate the ability of the neural network based robust fault diagnosis scheme to detect and accommodate faults in a two-link robotic manipulator.

16

Henry, D., C. Le Peuvédic, L. Strippoli, and F. Ankersen. "Robust Model-based Fault Diagnosis of Thruster Faults in Spacecraft." IFAC-PapersOnLine 48, no. 21 (2015): 1078–83. http://dx.doi.org/10.1016/j.ifacol.2015.09.670.

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17

Shahzad, Ebrahim, Adnan Umar Khan, Muhammad Iqbal, Ahmad Saeed, Ghulam Hafeez, Athar Waseem, Fahad R. Albogamy, and Zahid Ullah. "Sensor Fault-Tolerant Control of Microgrid Using Robust Sliding-Mode Observer." Sensors 22, no. 7 (March 25, 2022): 2524. http://dx.doi.org/10.3390/s22072524.

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This work investigates sensor fault diagnostics and fault-tolerant control for a voltage source converter based microgrid (model) using a sliding-mode observer. It aims to provide a diagnosis of multiple faults (i.e., magnitude, phase, and harmonics) occurring simultaneously or individually in current/potential transformers. A modified algorithm based on convex optimization is used to determine the gains of the sliding-mode observer, which utilizes the feasibility optimization or trace minimization of a Ricatti equation-based modification of H-Infinity (H∞) constrained linear matrix inequalities. The fault and disturbance estimation method is modified and improved with some corrections in previous works. The stability and finite-time reachability of the observers are also presented for the considered faulty and perturbed microgrid system. A proportional-integral (PI) based control is utilized for the conventional regulations required for frequency and voltage sags occurring in a microgrid. However, the same control block features fault-tolerant control (FTC) functionality. It is attained by incorporating a sliding-mode observer to reconstruct the faults of sensors (transformers), which are fed to the control block after correction. Simulation-based analysis is performed by presenting the results of state/output estimation, state/output estimation errors, fault reconstruction, estimated disturbances, and fault-tolerant control performance. Simulations are performed for sinusoidal, constant, linearly increasing, intermittent, sawtooth, and random sort of often occurring sensor faults. However, this paper includes results for the sinusoidal nature voltage/current sensor (transformer) fault and a linearly increasing type of fault, whereas the remaining results are part of the supplementary data file. The comparison analysis is performed in terms of observer gains being estimated by previously used techniques as compared to the proposed modified approach. It also includes the comparison of the voltage-frequency control implemented with and without the incorporation of the used observer based fault estimation and corrections, in the control block. The faults here are considered for voltage/current sensor transformers, but the approach works for a wide range of sensors.

18

Tang, Jing Yuan, Jian Ming Chen, and Cai Zhang. "Nonlinear Analog Circuit Fault Diagnosis Based on MFDFA Method." Applied Mechanics and Materials 263-266 (December 2012): 108–13. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.108.

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This paper presents a fault diagnosis method for nonlinear analog circuit based on multifractal detrended fluctuation analysis (MFDFA) method. The MFDFA method is applied to analysis fault signal and extracts the multifractal features from the raw signal. The selected features are given to SVM classifier for further classification. The data required to develop the classifier are generated by simulating various faults using Pspice software. The simulation results show that the proposed method provides a robust and accurate method for nonlinear circuit fault diagnosis.

19

Hsueh, Yu-Min, Veeresh Ramesh Ittangihal, Wei-Bin Wu, Hong-Chan Chang, and Cheng-Chien Kuo. "Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform." Symmetry 11, no. 10 (September 29, 2019): 1212. http://dx.doi.org/10.3390/sym11101212.

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Detecting the faults related to the operating condition of induction motors is a very important task for avoiding system failure. In this paper, a novel methodology is demonstrated to detect the working condition of a three-phase induction motor and classify it as a faulty or healthy motor. The electrical current signal data is collected for five different types of fault and one normal operating condition of the induction motors. The first part of the methodology illustrates a pattern recognition technique based on the empirical wavelet transform, to transform the raw current signal into two dimensional (2-D) grayscale images comprising the information related to the faults. Second, a deep CNN (Convolutional Neural Network) model is proposed to automatically extract robust features from the grayscale images to diagnose the faults in the induction motors. The experimental results show that the proposed methodology achieves a competitive accuracy in the fault diagnosis of the induction motors and that it outperformed the traditional statistical and other deep learning methods.

20

GERTLER, JANOS J., and MOID M. KUNWER. "Optimal residual decoupling for robust fault diagnosis." International Journal of Control 61, no. 2 (February 1995): 395–421. http://dx.doi.org/10.1080/00207179508921908.

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21

Sauter, D., F. Rambeaux, and F. Hamelin. "Robust Fault Diagnosis in an H ∞ Setting." IFAC Proceedings Volumes 30, no. 18 (August 1997): 869–74. http://dx.doi.org/10.1016/s1474-6670(17)42509-2.

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22

Sauter, Dominique, Shanbin Li, and Christophe Aubrun. "Robust fault diagnosis of networked control systems." International Journal of Adaptive Control and Signal Processing 23, no. 8 (August 2009): 722–36. http://dx.doi.org/10.1002/acs.1091.

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23

Bhushan, Mani, Sridharakumar Narasimhan, and Raghunathan Rengaswamy. "Robust sensor network design for fault diagnosis." Computers & Chemical Engineering 32, no. 4-5 (April 2008): 1067–84. http://dx.doi.org/10.1016/j.compchemeng.2007.06.020.

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24

Patton, Ron J., and Jie Chen. "On eigenstructure assignment for robust fault diagnosis." International Journal of Robust and Nonlinear Control 10, no. 14 (2000): 1193–208. http://dx.doi.org/10.1002/1099-1239(20001215)10:14<1193::aid-rnc523>3.0.co;2-r.

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25

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 (July 19, 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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Appiah, Albert Yaw, Xinghua Zhang, Ben Beklisi Kwame Ayawli, and Frimpong Kyeremeh. "Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques." International Journal of Photoenergy 2019 (February 18, 2019): 1–19. http://dx.doi.org/10.1155/2019/6953530.

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The environmentally clean nature of solar photovoltaic (PV) technology causes PV power generation to be embraced by all countries across the globe. Consequently, installation and utilization of PV power systems have seen much growth in recent years. Although PV arrays of such systems are robust, they are not immune to faults. To guarantee reliable power supply, economic returns, and safety of both humans and equipment, highly accurate fault detection, diagnosis, and interruption devices are required. In this paper, an overview of four major PV array faults and their causes are presented. Specifically, ground fault, line-line fault, arc fault, and hot spot fault have been covered. Next, conventional and advanced fault detection and diagnosis (FDD) techniques for managing these faults are reviewed. Moreover, a single evaluation metric has been proposed and utilized to evaluate the performances of the advanced FDD techniques. Finally, based on the papers reviewed, PV array fault management future trends and possible recommendations have been outlined.

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Hu, Kaiyu, Wenhao Li, and Zian Cheng. "Fuzzy adaptive fault diagnosis and compensation for variable structure hypersonic vehicle with multiple faults." PLOS ONE 16, no. 8 (August 13, 2021): e0256200. http://dx.doi.org/10.1371/journal.pone.0256200.

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Based on the type-II fuzzy logic, this paper proposes a robust adaptive fault diagnosis and fault-tolerant control (FTC) scheme for multisensor faults in the variable structure hypersonic vehicles with parameter uncertainties. Type-II fuzzy method approximates the original models while eliminating the parameter uncertainties. Hence the sensor faults are detected and isolated by the multiple output residuals and thresholds considering nonlinear approximation errors and disturbance. Based on the fuzzy adaptive augmented observer, the faults and disturbance are all estimated accurately by an improved proportional-differential part. Then a variable structure FTC scheme repairs the faults by the estimation, the fast-varying disturbance is considered in FTC scheme and is compensated by the control parameters designed based on its derivative function, thereby enhancing the output robust tracking accuracy of the variable structure hypersonic vehicles. The Lyapunov theory proves the system robust stability, semi-physical simulation verifies the validity of the proposed method and the superiority compared with the traditional method.

28

Mo, Li Li. "Transformer Fault Diagnosis Method Based on Support Vector Machine and Ant Colony." Advanced Materials Research 659 (January 2013): 54–58. http://dx.doi.org/10.4028/www.scientific.net/amr.659.54.

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For transformer fault diagnosis of the IEC three-ratio is an effective method in the dissolved gas analysis (DGA). But it does not offer completely objective, accurate diagnosis for all the faults. Aiming at parameters are confirmed by the cross validation, using the ant colony algorithm, the ACSVM-IEC method for the transformer fault diagnosis is proposed. Experimental results show that the proposed algorithm in this paper that can find out the optimum accurately in a wide range. The proposed approach is robust and practical for transformer fault diagnosis.

29

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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Liang, Shitong, and Jie Ma. "Compound Fault Diagnosis of Gearbox Based on RLMD and SSA-PNN." Mathematical Problems in Engineering 2021 (September 29, 2021): 1–9. http://dx.doi.org/10.1155/2021/3716033.

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In order to solve the difficulty in the classification of gearbox compound faults, a gearbox fault diagnosis method based on the sparrow search algorithm (SSA) improved probabilistic neural network (PNN) is proposed. Firstly, the gearbox fault signal is decomposed into a series of product functions (PFs) by robust local mean decomposition (RLMD). Then, the permutation entropy of PFs, which contains much fault information, is calculated to construct the feature vector and input it into the SSA-PNN model. The experimental results show that compared with the traditional fault diagnosis methods based on EMD-BP and EEMD-PNN, the gearbox fault diagnosis method based on RLMD and SSA-PNN has higher diagnosis accuracy.

31

Piltan, Farzin, Cheol-Hong Kim, and Jong-Myon Kim. "Adaptive Fuzzy-Based Fault-Tolerant Control of a Continuum Robotic System for Maxillary Sinus Surgery." Applied Sciences 9, no. 12 (June 19, 2019): 2490. http://dx.doi.org/10.3390/app9122490.

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Continuum robots represent a class of highly sensitive, multiple-degrees-of-freedom robots that are biologically inspired. Because of their flexibility and accuracy, these robots can be used in maxillary sinus surgery. The design of an effective procedure with high accuracy, reliability, robust fault diagnosis, and fault-tolerant control for a surgical robot for the sinus is necessary to maintain the high performance and safety necessary for surgery on the maxillary sinus. Thus, a robust adaptive hybrid observation method using an adaptive, fuzzy auto regressive with exogenous input (ARX) Laguerre Takagi–Sugeno (T–S) fuzzy robust feedback linearization observer for a surgical robot is presented. To address the issues of system modeling, the fuzzy ARX-Laguerre technique is represented. In addition, a T–S fuzzy robust feedback linearization observer is applied to a fuzzy ARX-Laguerre to improve the accuracy of fault estimation, reliability, and robustness for the surgical robot in the presence of uncertainties. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy observation-based feedback linearization technique is presented. The effectiveness of the proposed algorithm is tested with simulations. Experimental results show that the proposed method reduces the average position error from 35 mm to 2.45 mm in the presence of faults.

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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 (March 26, 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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Nguyen, Ngoc, and Sung Hong. "Fault Diagnosis and Fault-Tolerant Control Scheme for Quadcopter UAVs with a Total Loss of Actuator." Energies 12, no. 6 (March 23, 2019): 1139. http://dx.doi.org/10.3390/en12061139.

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Fault-tolerant control has drawn attention in recent years owning to its reliability and safe flight during missions. In this article, an active fault-tolerant control method is proposed to control a quadcopter in the presence of actuator faults and disturbances. Firstly, the dynamics of the quadcopter are presented. Secondly, a robust adaptive sliding mode Thau observer is presented to estimate the time-varying magnitudes of actuator faults. Thirdly, a fault-tolerant control scheme based on sliding mode control and reconfiguration technique is designed to maintain the quadcopter at the desired position despite the presence of faults. Unlike previous studies, the proposed method aims to integrate the fault diagnosis and a fault-tolerant control scheme into a single unit with total loss of actuator. Simulation results illustrate the efficiency of the suggested algorithm.

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Piltan, Farzin, and Jong-Myon Kim. "Hybrid Fault Diagnosis of Bearings: Adaptive Fuzzy Orthonormal-ARX Robust Feedback Observer." Applied Sciences 10, no. 10 (May 22, 2020): 3587. http://dx.doi.org/10.3390/app10103587.

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Rolling-element bearings (REBs) make up a class of non-linear rotating machines that can be applied in several activities. Conceding a bearing has complicated and uncertain behavior that exhibits substantial challenges to fault diagnosis. Thus, the offered anomaly-diagnosis algorithm, based on a fuzzy orthonormal-ARX adaptive fuzzy logic-structure feedback observer, is developed. A fuzzy orthonormal-ARX algorithm is presented for non-stationary signal modeling. Next, a robust, stable, reliable, and accurate observer is developed for signal estimation. Therefore, firstly, a classical feedback observer is implemented. To address the robustness drawback found in feedback observers, a multi-structure technique is developed. Furthermore, to generate signal estimation performance and reliability, the fuzzy logic technique is applied to the structure feedback observer. Also, to improve the stability, reliability, and robustness of the fuzzy orthonormal-ARX fuzzy logic-structure feedback observer, an adaptive algorithm is developed. After generating the residual signals, a support vector machine (SVM) is developed for the detection and classification of the bearing fault conditions. The effectiveness of the proposed procedure is validated using two different datasets for single-type fault diagnosis based on the Case Western Reverse University (CWRU) vibration dataset and multi-type fault diagnosis of bearing using the Smart Health Safety Environment (SHSE) Lab acoustic emission dataset. The proposed algorithm increases the classification accuracy from 86% in the SVM-based fuzzy orthonormal-ARX feedback observer to 97.5% in single-type fault and from 80% to 98.3% in the multi-type faults.

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Puig, Vicenç, and Joseba Quevedo. "Fault-Tolerant PID Controllers Using a Passive Robust Fault Diagnosis Approach." IFAC Proceedings Volumes 33, no. 4 (April 2000): 309–17. http://dx.doi.org/10.1016/s1474-6670(17)38262-9.

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Puig, Vicenç, and Joseba Quevedo. "Fault-tolerant PID controllers using a passive robust fault diagnosis approach." Control Engineering Practice 9, no. 11 (November 2001): 1221–34. http://dx.doi.org/10.1016/s0967-0661(01)00068-5.

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Yao, Lina, and Yawei Wu. "Robust fault diagnosis and fault‐tolerant control for uncertain multiagent systems." International Journal of Robust and Nonlinear Control 30, no. 18 (September 19, 2020): 8192–205. http://dx.doi.org/10.1002/rnc.5228.

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Ghanooni, Pooria, Hamed Habibi, Amirmehdi Yazdani, Hai Wang, Somaiyeh MahmoudZadeh, and Amin Mahmoudi. "Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers." Electronics 10, no. 21 (October 28, 2021): 2637. http://dx.doi.org/10.3390/electronics10212637.

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This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault- and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

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Zaccaria, Valentina, Amare Desalegn Fentaye, and Konstantinos Kyprianidis. "Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 2: Discrimination of Gradual Degradation and Rapid Faults." Machines 9, no. 12 (November 24, 2021): 308. http://dx.doi.org/10.3390/machines9120308.

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There are many challenges that an effective diagnostic system must overcome for successful fault diagnosis in gas turbines. Among others, it has to be robust to engine-to-engine variations in the fleet, it has to discriminate between gradual deterioration and abrupt faults, and it has to identify sensor faults correctly and be robust in case of such faults. To combine their benefits and overcome their limitations, two diagnostic methods were integrated in this work to form a multi-layer system. An adaptive performance model was used to track gradual deterioration and detect rapid or abrupt anomalies, while a series of static and dynamic Bayesian networks were integrated to identify component degradation, component abrupt faults, and sensor faults. The proposed approach was tested on synthetic data and field data from a single-shaft gas turbine of 50 MW class. The results showed that the approach could give acceptable accuracy in the isolation and identification of multiple faults, with 99% detection and isolation accuracy and 1% maximum error in the identified fault magnitude. The approach was also proven robust to sensor faults, by replacing the faulty signal with an estimated value that had only 3% error compared to the real measurement.

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Cartocci, Nicholas, Marcello R. Napolitano, Gabriele Costante, and Mario L. Fravolini. "A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation." Sensors 21, no. 5 (February 26, 2021): 1645. http://dx.doi.org/10.3390/s21051645.

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

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Lin, Rui Lin, and Ting Tao Ming. "Faults Diagnosis for Electro-Hydraulic Servo System Using Support Vector Machine Based on Robust Observer." Applied Mechanics and Materials 29-32 (August 2010): 2219–24. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2219.

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This paper presents a new method of fault diagnosis for electro-hydraulic servo system using support vector machine based on robust observer. Firstly, an electro-hydraulic system is introduced and a linear robust observer is designed for the system. Secondly, inner leakage and outer leakage faults of the system are experimentally simulated. Simulating results show that robust residual is sensitive to leakage faults but can not distinguish inner leakage and outer leakage in rod side. Thirdly, a nonlinear support vector machine was closely studied by regarding waveform and kurtosis index of displacement, residual, and pressure. Finally, educating and testing experiments show that the nonlinear support vector based on robust observer can diagnose all leakage faults of the system successfully.

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Nguyen, Ngoc Phi, and Sung Kyung Hong. "Sliding Mode Thau Observer for Actuator Fault Diagnosis of Quadcopter UAVs." Applied Sciences 8, no. 10 (October 11, 2018): 1893. http://dx.doi.org/10.3390/app8101893.

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Fault diagnosis (FD) is one of the main roles of fault-tolerant control (FTC) systems. An FD should not only identify the presence of a fault, but also quantify its magnitude and location. In this work, we present a robust fault diagnosis method for quadcopter unmanned aerial vehicle (UAV) actuator faults. The state equation of the quadcopter UAV is examined as a nonlinear system. An adaptive sliding mode Thau observer (ASMTO) method is proposed to estimate the fault magnitude through an adaptive algorithm. We then obtain the design matrices and parameters using the linear matrix inequalities (LMI) technique. Finally, experimental results are presented to show the advantages of the proposed algorithm. Unlike previous research on quadcopter UAV FD systems, our study is based on ASMTO and can, therefore, determine the time variability of a fault in the presence of external disturbances.

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Djeziri, Mohand Arab, Rochdi Merzouki, Belkacem Ould Bouamama, and Genevieve Dauphin-Tanguy. "Robust Fault Diagnosis by Using Bond Graph Approach." IEEE/ASME Transactions on Mechatronics 12, no. 6 (December 2007): 599–611. http://dx.doi.org/10.1109/tmech.2007.912746.

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Vemuri, Arun T., and Marios M. Polycarpou. "Robust nonlinear fault diagnosis in input-output systems." International Journal of Control 68, no. 2 (January 1997): 343–60. http://dx.doi.org/10.1080/002071797223659.

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Yin, Xiang, Jun Chen, Zhaojian Li, and Shaoyuan Li. "Robust Fault Diagnosis of Stochastic Discrete Event Systems." IEEE Transactions on Automatic Control 64, no. 10 (October 2019): 4237–44. http://dx.doi.org/10.1109/tac.2019.2893873.

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46

Chen, J., and R. J. Patton. "H ∞ Formulation and Solution for Robust Fault Diagnosis." IFAC Proceedings Volumes 32, no. 2 (July 1999): 7808–13. http://dx.doi.org/10.1016/s1474-6670(17)57332-2.

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Jiang, Jin. "Robust model-based fault diagnosis for dynamic systems." Automatica 38, no. 6 (June 2002): 1089–91. http://dx.doi.org/10.1016/s0005-1098(01)00290-4.

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Kovanic, P., and J. Pacovský. "Robust Filtering and Fault Diagnosis by Gnostical Methods." IFAC Proceedings Volumes 20, no. 5 (July 1987): 87–91. http://dx.doi.org/10.1016/s1474-6670(17)55357-4.

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Kyung Joo Mo, Gibaek Lee, Dong Soo Nam, Yeo Hong Yoon, and En Sup Yoon. "Robust fault diagnosis based on clustered symptom trees." Control Engineering Practice 5, no. 2 (February 1997): 199–208. http://dx.doi.org/10.1016/s0967-0661(97)00226-8.

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Doraiswami, R., and M. Stevenson. "A robust influence matrix approach to fault diagnosis." IEEE Transactions on Control Systems Technology 4, no. 1 (1996): 29–39. http://dx.doi.org/10.1109/87.481764.

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