Literatura académica sobre el tema "Fault detection/estimation"
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Artículos de revistas sobre el tema "Fault detection/estimation"
Zhang, Chuang, Xiubin Zhao, Chunlei Pang, Yong Wang, Liang Zhang y Bo Feng. "Improved Fault Detection Method Based on Robust Estimation and Sliding Window Test for INS/GNSS Integration". Journal of Navigation 73, n.º 4 (28 de febrero de 2020): 776–96. http://dx.doi.org/10.1017/s0373463319000778.
Texto completoMharakurwa, Edwell T., G. N. Nyakoe y A. O. Akumu. "Power Transformer Fault Severity Estimation Based on Dissolved Gas Analysis and Energy of Fault Formation Technique". Journal of Electrical and Computer Engineering 2019 (3 de febrero de 2019): 1–10. http://dx.doi.org/10.1155/2019/9674054.
Texto completoHajiyev, Chingiz, Demet Cilden-Guler y Ulviye Hacizade. "Two-Stage Kalman Filter for Fault Tolerant Estimation of Wind Speed and UAV Flight Parameters". Measurement Science Review 20, n.º 1 (1 de febrero de 2020): 35–42. http://dx.doi.org/10.2478/msr-2020-0005.
Texto completoLiu, Zhao y Anwar Sohel. "Application of MMAE to the Fault Detection of Lithium-Ion Battery". Applied Mechanics and Materials 598 (julio de 2014): 342–46. http://dx.doi.org/10.4028/www.scientific.net/amm.598.342.
Texto completoSwetapadma, Aleena y Anamika Yadav. "Fuzzy Inference System Approach for Locating Series, Shunt, and Simultaneous Series-Shunt Faults in Double Circuit Transmission Lines". Computational Intelligence and Neuroscience 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/620360.
Texto completoBurdusel, Constantin. "A Fault Detection Method for Attitude Sensors of Satellite". Applied Mechanics and Materials 325-326 (junio de 2013): 769–73. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.769.
Texto completoMinh, Vu Trieu, Nitin Afzulpurkar y W. M. Wan Muhamad. "Fault Detection and Control of Process Systems". Mathematical Problems in Engineering 2007 (2007): 1–20. http://dx.doi.org/10.1155/2007/80321.
Texto completoNikranjbar, A., M. Ebrahimi y A. S. Wood. "Model-based fault diagnosis of induction motor eccentricity using particle swarm optimization". Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 223, n.º 3 (1 de diciembre de 2008): 607–15. http://dx.doi.org/10.1243/09544062jmes1113.
Texto completoAsokan, A. y D. Sivakumar. "Model based fault detection and diagnosis using structured residual approach in a multi-input multi-output system". Serbian Journal of Electrical Engineering 4, n.º 2 (2007): 133–45. http://dx.doi.org/10.2298/sjee0702133a.
Texto completoZhu, Linhai, Jinfu Liu, Yujia Ma, Weixing Zhou y Daren Yu. "A Coupling Diagnosis Method for Sensor Faults Detection, Isolation and Estimation of Gas Turbine Engines". Energies 13, n.º 18 (22 de septiembre de 2020): 4976. http://dx.doi.org/10.3390/en13184976.
Texto completoTesis sobre el tema "Fault detection/estimation"
Zhou, Yilun. "Fault detection and distributed estimation with sensor networks". Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/61021.
Texto completoStocks, Mikael. "Stator fault detection and parameter estimation in induction machines". Licentiate thesis, Luleå, 2002. http://epubl.luth.se/1402-1757/2002/23.
Texto completoXiong, Jun. "Set-membership state estimation and application on fault detection". Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2013. http://tel.archives-ouvertes.fr/tel-01068054.
Texto completoShafiei, Mehdi. "Distribution network state estimation, time dependency and fault detection". Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/124659/2/Mehdi_Shafiei_Thesis.pdf.
Texto completoYang, Zaiyue y 楊再躍. "Fault detection, estimation and control of periodically excited nonlinear systems". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40887984.
Texto completoYang, Zaiyue. "Fault detection, estimation and control of periodically excited nonlinear systems". Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B40887984.
Texto completoSu, Jinya. "Fault estimation algorithms : design and verification". Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/23231.
Texto completoSalehpour, Soheil. "Fault detection and model quality estimation using mixed integer linear programming /". Luleå : Luleå University of Technology, 2009. http://pure.ltu.se/ws/fbspretrieve/2740260.
Texto completoZhang, Xiaoxia. "Incipient anomaly detection and estimation for complex system health monitoring". Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG025.
Texto completoIncipient fault detection and diagnosis in engineering and multivariate industrial systems with a high-level noise are addressed in this Ph.D. thesis by a ’global’ non-parametric statistical approach. An incipient fault is supposed to induce an abnormal change in the measured value of the system variable. However, such change is weak, and it tends not to cause obvious changes in the signal distribution’s parameters. Especially in high noise level environment, the weak fault feature can be masked by the noise and becomes unpredictable. In such a condition, using traditional parametric-based methods generally fails in the fault detection. To cope with incipient fault detection and diagnosis, a ’global’ approach that can consider the total faults signature is needed. The incipient fault detection can be obtained by measuring the differences between the signal distributions before and after the fault occurrence. Some distribution-based ’global’ methods have been proposed, however, the detection capabilities of these existed approaches in high noise level environment should be improved. In this context, Jensen-Shannon divergence is considered a ’global’ fault indicator to deal with the incipient fault detection and diagnosis in a high noise level environment. Its detection performance for small abnormal variations hidden in noise is validated through simulation. In addition, the fault estimation problem is also considered in this work. A theoretical fault severity estimation model depending on the divergence value for the Gaussian condition is derived. The accuracy of the estimation model is evaluated on numerical models through simulations. Then, the ’global’ statistical approach is applied to two applications in engineering. The first one relates to non- destruction incipient cracks detection. The Jensen-Shannon divergence combined with Noisy Independent Component Analysis and Wavelet analysis was applied for detection and characterization of minor cracks in conductive structures with high-level perturbations based on experimental impedance signals. The second application addresses the incipient fault diagnosis in a multivariate non-linear process with a high-level noise. Tennessee Eastman Process (TEP) is one typical multivariate non-linear process, the Jensen-Shannon divergence in the Kernel Principal Component Analysis (KPCA) is developed for coping with incipient fault detection in this process
Törnqvist, David. "Statistical Fault Detection with Applications to IMU Disturbances". Licentiate thesis, Linköping University, Linköping University, Automatic Control, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7094.
Texto completoThis thesis deals with the problem of detecting faults in an environment where the measurements are affected by additive noise. To do this, a residual sensitive to faults is derived and statistical methods are used to distinguish faults from noise. Standard methods for fault detection compare a batch of data with a model of the system using the generalized likelihood ratio. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. One method to handle this is the parity-space method which solves the problem by removing the influence of the initial state using a projection.
In this thesis, the case where prior knowledge about the initial state is available is treated. This can be obtained for example from a Kalman filter. Combining the prior estimate with a minimum variance estimate from the data batch results in a smoothed estimate. The influence of the estimated initial state is then removed. It is also shown that removing the influence of the initial state by an estimate from the data batch will result in the parity-space method. To model slowly changing faults, an efficient parameterization using Chebyshev polynomials is given.
The methods described above have been applied to an Inertial Measurement Unit, IMU. The IMU usually consists of accelerometers and gyroscopes, but has in this work been extended with a magnetometer. Traditionally, the IMU has been used to estimate position and orientation of airplanes, missiles etc. Recently, the size and cost has decreased making it possible to use IMU:s for applications such as augmented reality and body motion analysis. Since a magnetometer is very sensitive to disturbances from metal, such disturbances have to be detected. Detection of the disturbances makes compensation possible. Another topic covered is the fundamental question of observability for fault inputs. Given a fixed or linearly growing fault, conditions for observability are given.
The measurements from the IMU show that the noise distribution of the sensors can be well approximated with white Gaussian noise. This gives good correspondence between practical and theoretical results when the sensor is kept at rest. The disturbances for the IMU can be approximated using smooth functions with respect to time. Low rank parameterizations can therefore be used to describe the disturbances. The results show that the use of smoothing to obtain the initial state estimate and parameterization of the disturbances improves the detection performance drastically.
Libros sobre el tema "Fault detection/estimation"
Anton, Stoorvogel y Sannuti Peddapullaiah 1941-, eds. Filtering theory: With applications to fault detection, isolation, and estimation. Boston ; Berlin: Birkhäuser, 2007.
Buscar texto completoC, Merrill Walter, Duyar Ahmet y United States. National Aeronautics and Space Administration., eds. A distributed fault-detection and diagnosis system using on-line parameter estimation. [Washington, D.C: National Aeronautics and Space Administration, 1991.
Buscar texto completoC, Merrill Walter, Duyar Ahmet y United States. National Aeronautics and Space Administration., eds. A distributed fault-detection and diagnosis system using on-line parameter estimation. [Washington, D.C: National Aeronautics and Space Administration, 1991.
Buscar texto completoNeural network-based state estimation of nonlinear systems: Application to fault detection and isolation. New York: Springer, 2010.
Buscar texto completoLehrasab, Nadeem. A generic fault detection and isolation approach for single-throw mechanical equipment. Birmingham: University of Birmingham, 1999.
Buscar texto completoStoorvogel, Anton A., Peddapullaiah Sannuti y Ali Saberi. Filtering Theory: With Applications to Fault Detection, Isolation, and Estimation. Springer London, Limited, 2007.
Buscar texto completoA distributed fault-detection and diagnosis system using on-line parameter estimation. [Washington, D.C: National Aeronautics and Space Administration, 1991.
Buscar texto completoCapítulos de libros sobre el tema "Fault detection/estimation"
Mahmoud, Magdi S. "Fuzzy Fault Detection and Control". En Fuzzy Control, Estimation and Diagnosis, 483–546. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54954-5_9.
Texto completoDing, Steven X. "Basic Requirements on Fault Detection and Estimation". En Advanced methods for fault diagnosis and fault-tolerant control, 31–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62004-5_2.
Texto completoIsermann, Rolf. "Fault detection with state observers and state estimation". En Fault-Diagnosis Systems, 231–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-30368-5_11.
Texto completoDing, Steven X. "Basic Methods for Fault Detection and Estimation in Static Processes". En Advanced methods for fault diagnosis and fault-tolerant control, 45–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62004-5_3.
Texto completoWei, M., D. Lapucha y H. Martell. "Fault Detection and Estimation in Dynamic Systems". En Kinematic Systems in Geodesy, Surveying, and Remote Sensing, 201–17. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3102-8_19.
Texto completoGiua, Alessandro. "State Estimation and Fault Detection Using Petri Nets". En Applications and Theory of Petri Nets, 38–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21834-7_3.
Texto completoTalebi, Heidar A., Farzaneh Abdollahi, Rajni V. Patel y Khashayar Khorasani. "A Robust Actuator Gain Fault Detection and Isolation Scheme". En Neural Network-Based State Estimation of Nonlinear Systems, 83–98. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1438-5_5.
Texto completoLiu, Yang, Zidong Wang y Donghua Zhou. "Filtering and Fault Detection for Nonlinear Systems with Polynomial Approximation". En State Estimation and Fault Diagnosis under Imperfect Measurements, 89–114. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003309482-6.
Texto completoLiu, Zhigang. "Slide Plate Fault Detection of Pantograph Based on Image Processing". En Detection and Estimation Research of High-speed Railway Catenary, 109–37. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2753-6_5.
Texto completoIsermann, Rolf. "Experiences with Process Fault Detection Methods via Parameter Estimation". En System Fault Diagnostics, Reliability and Related Knowledge-Based Approaches, 3–33. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3929-5_1.
Texto completoActas de conferencias sobre el tema "Fault detection/estimation"
Zhang, Xiaodong, Remus C. Avram, Liang Tang y Michael J. Roemer. "A Unified Nonlinear Approach to Fault Diagnosis of Aircraft Engines". En ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-95803.
Texto completoXin Wang, Shuli Sun y Ying Shi. "Fault detection and noise variance identifier with cooperation fault-torlerance for multisensor system". En 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280148.
Texto completoGadsden, S. A. y S. R. Habibi. "State Estimation and Fault Detection of an Electrohydrostatic Actuator". En ASME/BATH 2014 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/fpmc2014-7847.
Texto completoGupta, Aniket, Karolos Grigoriadis, Matthew Franchek y Daniel J. Smith. "Online Adaptive Model Based Fault Detection, Isolation and Estimation Method". En 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.
Texto completoChunxia Wang, Chenglin Wen y Yang Lu. "A fault diagnosis method by using extreme learning machine". En 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280215.
Texto completoNoel, Nana K., Kari Tammi, Gregory D. Buckner y Nathan S. Gibson. "Intelligent Kalman Filtering for Fault Detection on an Active Magnetic Bearing System". En ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2122.
Texto completoGarimella, Phanindra y Bin Yao. "Fault Detection of an Electro-Hydraulic Cylinder Using Adaptive Robust Observers". En ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-61718.
Texto completoKobayashi, Takahisa y Donald L. Simon. "Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics". En ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38550.
Texto completoTian, Kun y Hai-hua Yu. "Robust non-fragile fault-tolerant H∞ control for time-delay uncertain linear systems". En 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280214.
Texto completoTornqvist, David, Saikat Saha y Fredrik Gustafsson. "Fault detection using nonlinear parameter estimation". En 2011 IEEE Aerospace Conference. IEEE, 2011. http://dx.doi.org/10.1109/aero.2011.5747438.
Texto completoInformes sobre el tema "Fault detection/estimation"
Jenkins, Cody David. Bearing Fault Detection and Wear Estimation Using Machine Learning. Office of Scientific and Technical Information (OSTI), agosto de 2019. http://dx.doi.org/10.2172/1557163.
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