Relevant bibliographies by topics / Robust fault diagnosis

Academic literature on the topic 'Robust fault diagnosis'

Author: Grafiati
Published: 20 May 2022
Last updated: 20 February 2023

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "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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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.
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Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Robust fault diagnosis":

1

Sun, Xiaoyu. "Unknown input observer approaches to robust fault diagnosis." Thesis, University of Hull, 2013. http://hydra.hull.ac.uk/resources/hull:8021.

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This thesis focuses on the development of the model-based fault detection and isolation /fault detection and diagnosis (FDI/FDD) techniques using the unknown input observer (UIO) methodology. Using the UI de-coupling philosophy to tackle the robustness issue, a set of novel fault estimation (FE)-oriented UIO approaches are developed based on the classical residual generation-oriented UIO approach considering the time derivative characteristics of various faults. The main developments proposed are: - Implement the residual-based UIO design on a high fidelity commercial aircraft benchmark model to detect and isolate the elevator sensor runaway fault. The FDI design performance is validated using a functional engineering simulation (FES) system environment provided through the activity of an EU FP7 project Advanced Fault Diagnosis for Safer Flight Guidance and Control (ADDSAFE). - Propose a linear time-invariant (LTI) model-based robust fast adaptive fault estimator (RFAFE) with UI de-coupling to estimate the aircraft elevator oscillatory faults considered as actuator faults. - Propose a UI-proportional integral observer (UI-PIO) to estimate actuator multiplicative faults based on an LTI model with UI de-coupling and with added H∞ optimisation to reduce the effects of the sensor noise. This is applied to an example on a hydraulic leakage fault (multiplicative fault) in a wind turbine pitch actuator system, assuming that thefirst derivative of the fault is zero. - Develop an UI–proportional multiple integral observer (UI-PMIO) to estimate the system states and faults simultaneously with the UI acting on the system states. The UI-PMIO leads to a relaxed condition of requiring that the first time derivative of the fault is zero instead of requiring that the finite time fault derivative is zero or bounded. - Propose a novel actuator fault and state estimation methodology, the UI–proportional multiple integral and derivative observer (UI-PMIDO), inspired by both of the RFAFE and UI-PMIO designs. This leads to an observer with the comprehensive feature of estimating faults with bounded finite time derivatives and ensuring fast FE tracking response. - Extend the UI-PMIDO theory based on LTI modelling to a linear parameter varying (LPV) model approach for FE design. A nonlinear two-link manipulator example is used to illustrate the power of this method.
2

Rajaraman, Srinivasan. "Robust model-based fault diagnosis for chemical process systems." Texas A&M University, 2003. http://hdl.handle.net/1969.1/3956.

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Fault detection and diagnosis have gained central importance in the chemical process industries over the past decade. This is due to several reasons, one of them being that copious amount of data is available from a large number of sensors in process plants. Moreover, since industrial processes operate in closed loop with appropriate output feedback to attain certain performance objectives, instrument faults have a direct effect on the overall performance of the automation system. Extracting essential information about the state of the system and processing the measurements for detecting, discriminating, and identifying abnormal readings are important tasks of a fault diagnosis system. The goal of this dissertation is to develop such fault diagnosis systems, which use limited information about the process model to robustly detect, discriminate, and reconstruct instrumentation faults. Broadly, the proposed method consists of a novel nonlinear state and parameter estimator coupled with a fault detection, discrimination, and reconstruction system. The first part of this dissertation focuses on designing fault diagnosis systems that not only perform fault detection and isolation but also estimate the shape and size of the unknown instrument faults. This notion is extended to nonlinear processes whose structure is known but the parameters of the process are a priori uncertain and bounded. Since the uncertainty in the process model and instrument fault detection interact with each other, a novel two-time scale procedure is adopted to render overall fault diagnosis. Further, some techniques to enhance the convergence properties of the proposed state and parameter estimator are presented. The remaining part of the dissertation extends the proposed model-based fault diagnosis methodology to processes for which first principles modeling is either expensive or infeasible. This is achieved by using an empirical model identification technique called subspace identification for state-space characterization of the process. Finally the proposed methodology for fault diagnosis has been applied in numerical simulations to a non-isothermal CSTR (continuous stirred tank reactor), an industrial melter process, and a debutanizer plant.
3

Willcox, Simon Ware. "Robust sensor fault diagnosis for aircraft based on analytical redundancy." Thesis, University of York, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329855.

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Chen, Jie. "Robust residual generation for model-based fault diagnosis of dynamic systems." Thesis, University of York, 1995. http://etheses.whiterose.ac.uk/2468/.

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Sadrnia, Mohammad Ali. "Robust fault diagnosis observer design using H#infinity# optimisation and #mu# synthesis." Thesis, University of Hull, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363208.

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Odofin, Sarah. "Robust fault diagnosis by GA optimisation with applications to wind turbine systems and induction motors." Thesis, Northumbria University, 2016. http://nrl.northumbria.ac.uk/36111/.

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This investigation focuses and analyses the theoretical and practical performance of a dynamic system, which affords condition monitoring and robust fault diagnosis. The importance of robustness in fault diagnosis is becoming significant for controlled dynamic systems in order to improve operating reliability, critical-safety and reducing the cost often caused by interruption shut down and component repairing. There is a strong motivation to develop an effective real-time monitoring and fault diagnosis strategy so as to ensure a timely response by supervisory personnel to false alarms and damage control due to faults/malfunctions. Environmental disturbances/noises are unavoidable in practical engineering systems, the effects of which usually reduce the diagnostic ability of conventional fault diagnosis algorithms, and even cause false alarms. As a result, robust fault diagnosis is vital for practical application in control systems, which aims to maximize the fault detectability and minimize the effects of environment disturbances/noises. In this study, a genetic algorithm (GA) optimization model-based fault diagnosis algorithm is investigated for applications in wind turbine energy systems and induction motors through concerns for typical types of developing (incipient) and sudden (abrupt) faults. A robust fault detection approach is utilized by seeking an optimal observer gain when GA optimisation problems become solvable so that the residual is sensitive to the faults, but robust against environmental disturbances/noises. Also, robust fault estimation techniques are proposed by integrating augmented observer and GA optimisation techniques so that the estimation error dynamics have a good robustness against environmental disturbances/noises. The two case studies investigated in this project are: a 5MW wind turbine model where robust fault detection and robust fault estimation are discussed with details; and a 2kW induction motor experimental setup is investigated, where robust fault detection and robust fault estimation are both examined, and modelling errors are effectively attenuated by using the proposed algorithms. The simulations and experimental results have demonstrated the effectiveness of the proposed fault diagnosis methods.
7

Ablay, Gunyaz. "Sliding Mode Approaches for Robust Control, State Estimation, Secure Communication, and Fault Diagnosis in Nuclear Systems." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1354551858.

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Kmelnitsky, Vitaly M. "Automated On-line Diagnosis and Control Configuration in Robotic Systems Using Model Based Analytical Redundancy." Digital WPI, 2002. https://digitalcommons.wpi.edu/etd-theses/167.

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Because of the increasingly demanding tasks that robotic systems are asked to perform, there is a need to make them more reliable, intelligent, versatile and self-sufficient. Furthermore, throughout the robotic system?s operation, changes in its internal and external environments arise, which can distort trajectory tracking, slow down its performance, decrease its capabilities, and even bring it to a total halt. Changes in robotic systems are inevitable. They have diverse characteristics, magnitudes and origins, from the all-familiar viscous friction to Coulomb/Sticktion friction, and from structural vibrations to air/underwater environmental change. This thesis presents an on-line environmental Change, Detection, Isolation and Accommodation (CDIA) scheme that provides a robotic system the capabilities to achieve demanding requirements and manage the ever-emerging changes. The CDIA scheme is structured around a priori known dynamic models of the robotic system and the changes (faults). In this approach, the system monitors its internal and external environments, detects any changes, identifies and learns them, and makes necessary corrections into its behavior in order to minimize or counteract their effects. A comprehensive study is presented that deals with every stage, aspect, and variation of the CDIA process. One of the novelties of the proposed approach is that the profile of the change may be either time or state-dependent. The contribution of the CDIA scheme is twofold as it provides robustness with respect to unmodeled dynamics and with respect to torque-dependent, state-dependent, structural and external environment changes. The effectiveness of the proposed approach is verified by the development of the CDIA scheme for a SCARA robot. Results of this extensive numerical study are included to verify the applicability of the proposed scheme.
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Xiong, Yi. "Robust fault diagnosis in linear and nonlinear systems based on unknown input, and sliding mode functional observer methodologies." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ61696.pdf.

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Wang, Ye. "Advances in state estimation, diagnosis and control of complex systems." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/669680.

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This dissertation intends to provide theoretical and practical contributions on estimation, diagnosis and control of complex systems, especially in the mathematical form of descriptor systems. The research is motivated by real applications, such as water networks and power systems, which require a control system to provide a proper management able to take into account their specific features and operating limits in presence of uncertainties related to their operation and failures from component malfunctions. Such a control system is expected to provide an optimal operation to obtain efficient and reliable performance. State estimation is an essential tool, which can be used not only for fault diagnosis but also for the controller design. To achieve a satisfactory robust performance, set theory is chosen to build a general framework for descriptor systems subject to uncertainties. Under certain assumptions, these uncertainties are propagated and bounded by deterministic sets that can be explicitly characterized at each iteration step. Moreover, set-invariance characterizations for descriptor systems are also of interest to describe the steady performance, which can also be used for active mode detection. For the controller design for complex systems, new developments of economic model predictive control (EMPC) are studied taking into account the case of underlying periodic behaviors. The EMPC controller is designed to be recursively feasible even with sudden changes in the economic cost function and the closed-loop convergence is guaranteed. Besides, a robust technique is plugged into the EMPC controller design to maintain these closed-loop properties in presence of uncertainties. Engineering applications modeled as descriptor systems are presented to illustrate these control strategies. From the real applications, some additional difficulties are solved, such as using a two-layer control strategy to avoid binary variables in real-time optimizations and using nonlinear constraint relaxation to deal with nonlinear algebraic equations in the descriptor model. Furthermore, the fault-tolerant capability is also included in the controller design for descriptor systems by means of the designed virtual actuator and virtual sensor together with an observer-based delayed controller.
Esta tesis propone contribuciones de carácter teórico y aplicado para la estimación del estado, el diagnóstico y el control óptimo de sistemas dinámicos complejos en particular, para los sistemas descriptores, incluyendo la capacidad de tolerancia a fallos. La motivación de la tesis proviene de aplicaciones reales, como redes de agua y sistemas de energía, cuya naturaleza crítica requiere necesariamente un sistema de control para una gestión capaz de tener en cuenta sus características específicas y límites operativos en presencia de incertidumbres relacionadas con su funcionamiento, así como fallos de funcionamiento de los componentes. El objetivo es conseguir controladores que mejoren tanto la eficiencia como la fiabilidad de dichos sistemas. La estimación del estado es una herramienta esencial que puede usarse no solo para el diagnóstico de fallos sino también para el diseño del control. Con este fin, se ha decidido utilizar metodologías intervalares, o basadas en conjuntos, para construir un marco general para los sistemas de descriptores sujetos a incertidumbres desconocidas pero acotadas. Estas incertidumbres se propagan y delimitan mediante conjuntos que se pueden caracterizar explícitamente en cada instante. Por otra parte, también se proponen caracterizaciones basadas en conjuntos invariantes para sistemas de descriptores que permiten describir comportamientos estacionarios y resultan útiles para la detección de modos activos. Se estudian también nuevos desarrollos del control predictivo económico basado en modelos (EMPC) para tener en cuenta posibles comportamientos periódicos en la variación de parámetros o en las perturbaciones que afectan a estos sistemas. Además, se demuestra que el control EMPC propuesto garantiza la factibilidad recursiva, incluso frente a cambios repentinos en la función de coste económico y se garantiza la convergencia en lazo cerrado. Por otra parte, se utilizan técnicas de control robusto pata garantizar que las estrategias de control predictivo económico mantengan las prestaciones en lazo cerrado, incluso en presencia de incertidumbre. Los desarrollos de la tesis se ilustran con casos de estudio realistas. Para algunas de aplicaciones reales, se resuelven dificultades adicionales, como el uso de una estrategia de control de dos niveles para evitar incluir variables binarias en la optimización y el uso de la relajación de restricciones no lineales para tratar las ecuaciones algebraicas no lineales en el modelo descriptor en las redes de agua. Finalmente, se incluye también una contribución al diseño de estrategias de control con tolerancia a fallos para sistemas descriptores.
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Books on the topic "Robust fault diagnosis":

1

Chen, Jie. Robust Model-Based Fault Diagnosis for Dynamic Systems. Boston, MA: Springer US, 1999.

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Abbott, Kathy H. Robust fault diagnosis of physical systems in operation. Hampton, Va: NASA Langley Research Center, 1991.

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Chen, Jie, and Ron J. Patton. Robust Model-Based Fault Diagnosis for Dynamic Systems. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5149-2.

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Chen, J. Robust model-based fault diagnosis for dynamic systems. Boston: Kluwer Academic Publishers, 1999.

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Mrugalski, Marcin. Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01547-7.

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Zhang, Jian, Akshya Kumar Swain, and Sing Kiong Nguang. Robust Observer-Based Fault Diagnosis for Nonlinear Systems Using MATLAB®. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32324-4.

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Ginart, Antonio, ed. Fault Diagnosis for Robust Inverter Power Drives. Institution of Engineering and Technology, 2018. http://dx.doi.org/10.1049/pbpo120e.

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Ginart, Antonio. Fault Diagnosis for Robust Inverter Power Drives. Institution of Engineering & Technology, 2018.

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Center, Langley Research, ed. Robust fault diagnosis of physical systems in operation. Hampton, Va: NASA Langley Research Center, 1991.

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Advanced Neural Networkbased Computational Schemes For Robust Fault Diagnosis. Springer International Publishing AG, 2013.

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Book chapters on the topic "Robust fault diagnosis":

1

Suchomski, Piotr, and Zdzisław Kowalczuk. "Robust H ∞-Optimal Synthesis of FDI Systems." In Fault Diagnosis, 261–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18615-8_7.

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Ding, Steven X. "Robust Fault Diagnosis and Control." In Encyclopedia of Systems and Control, 1214–18. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_158.

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Ding, Steven X. "Robust Fault Diagnosis and Control." In Encyclopedia of Systems and Control, 1–8. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5102-9_158-1.

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Ding, Steven X. "Robust Fault Diagnosis and Control." In Encyclopedia of Systems and Control, 1–5. London: Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-5102-9_158-2.

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Ding, Steven X. "Robust Fault Diagnosis and Control." In Encyclopedia of Systems and Control, 1954–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_158.

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Mhaskar, Prashant, Jinfeng Liu, and Panagiotis D. Christofides. "Fault Diagnosis and Robust Safe-Parking." In Fault-Tolerant Process Control, 105–24. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4808-1_6.

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Patton, R. J., and S. M. Kangethe. "Robust Fault Diagnosis in Dynamic Systems." In Failsafe Control Systems, 117–43. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-009-0429-3_10.

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Lan, Jianglin, and Ronald J. Patton. "Robust Integration in Fault Diagnosis and FTC." In Robust Integration of Model-Based Fault Estimation and Fault-Tolerant Control, 27–41. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58760-4_2.

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Yu, Dingli, and D. N. Shields. "Optimally Robust Fault Diagnosis Using Genetic Algorithms." In Artificial Neural Nets and Genetic Algorithms, 104–7. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_29.

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Ben-Haim, Yakov. "Fault Diagnosis, System Identification and Reliability Testing." In Robust Reliability in the Mechanical Sciences, 97–154. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61154-4_5.

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Conference papers on the topic "Robust fault diagnosis":

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Reddy, T. Agami. "Comparison of Two Model Based Automated Fault Detection and Diagnosis Methods for Centrifugal Chillers." In ASME 2008 2nd International Conference on Energy Sustainability collocated with the Heat Transfer, Fluids Engineering, and 3rd Energy Nanotechnology Conferences. ASMEDC, 2008. http://dx.doi.org/10.1115/es2008-54002.

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Research has been ongoing during the last several years on developing robust automated fault detecting and diagnosing (FDD) methods applicable for process faults in chillers used in commercial buildings. These FDD methods involve using sensor data from available thermal, pressure and electrical measurements from commercial chillers to compute characteristic features (CF) which allow more robust and sensitive fault detection than using the basic sensor data itself. One of the proposed methods is based on the analytical redundancy approach using polynomial black-box multiple linear regression models for each CF that are identified from fault-free data in conjunction with a diagnosis table. The second method is based on a classification approach involving linear discriminant analysis to identify the classification models whereby both the detection and diagnosis can be done simultaneously. This paper describes the mathematical basis of both methods, illustrates how they are to be tuned using the same fault-free data set in conjunction with limited faulty data, and then compares their performance when applied to different fault severity levels. The relative advantages and disadvantages of each method are highlighted and future development needs are pointed out.
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Tzortzis, Ioannis, and Marios M. Polycarpou. "Distributionally Robust Active Fault Diagnosis." In 2019 18th European Control Conference (ECC). IEEE, 2019. http://dx.doi.org/10.23919/ecc.2019.8796113.

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Chen, Tian, and Jian-Guo Sun. "Rough Set and Neural Network Based Fault Diagnosis for Aeroengine Gas Path." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68192.

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Aeroengine is a very complex nonlinear object. Traditional methods for its fault diagnosis are proved time-consuming and low efficient. A new system based on rough sets and neural networks for the fault diagnosis of aeroengine gas path is presented in this paper. At first, the rough set theory is used to determine qualitatively fault and isolate the fault. It consists of three steps: discretizing sensed data, reducing the decision table and generating rules. After that, feed-forward neural networks are added into the system to construct several sub-systems, which take the engine sensible data pretreated by rough sets as inputs and compute damage degrees of the aeroengine fault state. Last, the noise rejection abilities of the engine fault diagnosis system are analyzed. The test results show that the system can quantitatively diagnose the faults of aeroengine gas path with precision and efficiency, while it is robust for noise rejection.
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Zhang, Jiyu, Giorgio Rizzoni, and Qadeer Ahmed. "Fault Modelling for Hierarchical Fault Diagnosis and Prognosis." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3825.

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Fault modeling, which is the determination of the effects of a fault on a system, is an effective way for conducting failure analysis and fault diagnosis for complex system. One of the major challenges of fault modeling in complex systems is the ability to model the effects of component-level faults on the system. This paper develops a simulation-based methodology for failure analysis through modeling component-level fault effect on the system level, with application to electric vehicle powertrains. To investigate how a component fault such as short circuit in a power switch or open circuit in a motor winding affects the vehicle system, this paper develops a detailed simulator which allows us to see system and subsystem failure behaviors by incorporating fault models in the system. This fault modeling process provides useful knowledge for designing a reliable and robust fault diagnosis and prognosis procedures for electrified powertrains.
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Sadrnia, M. A. "Robust fault diagnosis observer design using H." In IEE Colloquium on Modelling and Signal Processing for Fault Diagnosis. IEE, 1996. http://dx.doi.org/10.1049/ic:19961379.

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Zhao, Hao, Weifei Hu, Zhenyu Liu, and Jianrong Tan. "A CapsNet-Based Fault Diagnosis Method for a Digital Twin of a Wind Turbine Gearbox." In ASME 2021 Power Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/power2021-66029.

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Abstract Accurate fault diagnosis of complex energy systems, such as wind turbines, is essential to avoid catastrophic accidents and ensure a stable power source. However, accurate fault diagnoses under dynamic operating conditions and various failure mechanisms are major challenges for wind turbines nowadays. Here we present a CapsNet-based deep learning scheme for data-driven fault diagnosis used in a digital twin of a wind turbine gearbox. The CapsNet model can extract the multi-dimensional features and rich spatial information from the gearbox monitoring data by an artificial neural network named the CapsNet. Through the dynamic routing algorithm between capsules, the network structure and parameters of the CapsNet model can be adjusted effectively to realize an accurate and robust classification of the operational conditions of a wind turbine gearbox, including front box stuck (single fault) and high-speed shaft bearing damage & planetary gear damage (coupling faults). Two gearbox datasets are used to verify the performance of the CapsNet model. The experimental results show that the accuracy of this proposed method is up to 98%, which proves the accuracy of CapsNet model in the case study when this model performed three-state classification (health, stuck, and coupled damage). Compared with state-of-the-art fault diagnosis methods reported in the literature, the CapsNet model has a competitive advantage, especially in the ability to diagnose coupling faults, high-speed shaft bearing damage & planetary gear damage in our case study. CapsNet has at least 2.4 percentage points higher than any other measure in our experiment. In addition, the proposed method can automatically extract features from the original monitoring data, and do not rely on expert experience or signal processing related knowledge, which provides a new avenue for constructing an accurate and efficient digital twin of wind turbine gearboxes.
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Hashemi, Seyed Reza, Ashkan Nazari, Roja Esmaeeli, Haniph Aliniagerdroudbari, Muapper Alhadri, Waleed Zakri, Abdul Haq Mohammed, Ajay Mahajan, and Siamak Farhad. "Fast Fault Diagnosis of a Lithium-Ion Battery for Hybrid Electric Aircraft." In ASME 2018 Power Conference collocated with the ASME 2018 12th International Conference on Energy Sustainability and the ASME 2018 Nuclear Forum. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/power2018-7476.

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A well-designed battery management system along with a set of voltage and current sensors is required to properly measure and control the battery cell operational variables for Hybrid Electric Aircrafts (HEAs). Some critical functions of the battery including State-Of-Charge (SOC) and State-Of-Health (SOH) estimations, over-current, and over-/under-voltage protections are mainly related to current and voltage sensor measurements. Therefore, in case of battery faults occur in HEA, designing a reliable and robust diagnostic procedure is essential. In this study, for Li-ion batteries, a new and fast fault diagnosis technique via collecting data is proposed. Finally, the effectiveness of the proposed diagnostic method is validated, and the results show how overcharge, over-discharge and sensor faults can be accurately detected.
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Gupta, Aniket, Karolos Grigoriadis, Matthew Franchek, and Daniel J. Smith. "Online Adaptive Model Based Fault Detection, Isolation and Estimation Method." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6080.

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In the present study a methodology to perform fault detection, isolation and estimation is proposed that is based on adaptive model based techniques. Fault detection and diagnostics is performed by comparing the coefficients of healthy system model with that of adapted online coefficients. This approach is shown to be robust to modeling errors, sensor noise and process variability. The proposed approach is applied to FTP-75 cycle simulation data of exhaust gas recircultaion (EGR) faults and is shown to effectively perform fault detection and diagnosis.
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Muddappa, Vinay K. S., and Sohel Anwar. "Electrochemical Model Based Fault Diagnosis of Li-Ion Battery Using Fuzzy Logic." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-37134.

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There is a strong urge for advanced diagnosis method, especially in high power battery packs and high energy density cell design applications, such as electric vehicle (EV) and hybrid electric vehicle segment, due to safety concerns. Accurate and robust diagnosis methods are required in order to optimize battery charge utilization and improve EV range. Battery faults cause significant model parameter variation affecting battery internal states and output. This work is focused on developing diagnosis method to reliably detect various faults inside lithiumion cell using electrochemical model based observer and fuzzy logic algorithm, which is implementable in real-time. The internal states and outputs from battery plant model were compared against those from the electrochemical model based observer to generate the residuals. These residuals and states were further used in a fuzzy logic based residual evaluation algorithm in order to detect the battery faults. Simulation results show that the proposed methodology is able to detect various fault types including overcharge, over-discharge and aged battery quickly and reliably, thus providing an effective and accurate way of diagnosing Li-Ion battery faults.
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Zhrabok, Alexey, Alexey Shumsky, and Alexey Suvorov. "Robust fault diagnosis in time-delay systems." In 2015 10th Asian Control Conference (ASCC). IEEE, 2015. http://dx.doi.org/10.1109/ascc.2015.7244420.

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Reports on the topic "Robust fault diagnosis":

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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.

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