Relevant bibliographies by topics / Fault detection/estimation

Academic literature on the topic 'Fault detection/estimation'

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Published: 18 April 2023

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Journal articles on the topic "Fault detection/estimation"

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Zhang, Chuang, Xiubin Zhao, Chunlei Pang, Yong Wang, Liang Zhang, and Bo Feng. "Improved Fault Detection Method Based on Robust Estimation and Sliding Window Test for INS/GNSS Integration." Journal of Navigation 73, no. 4 (February 28, 2020): 776–96. http://dx.doi.org/10.1017/s0373463319000778.

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Real-time and accurate fault detection and isolation is very important to ensure the reliability and precision of integrated inertial navigation and global navigation satellite systems. In this paper, the detection performance of a residual chi-square method is analysed, and on this basis an improved method of fault detection is proposed. The local test based on a standardised residual is introduced to detect and identify faulty measurements directly. Differing from the traditional method, two appropriate thresholds are selected to calculate the weight factor of each measurement, and the gain matrix is adjusted adaptively to reduce the influence of the undetected faulty measurement. The sliding window test, which uses past measurements, is also added to further improve the fault detection performance for small faults when the local test based on current measurements cannot judge whether a fault has occurred or not. Several simulations are conducted to evaluate the proposed method. The results show that the improved method has better fault detection performance than the traditional detection method, especially for small faults, and can improve the reliability and precision of the navigation system effectively.
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Mharakurwa, Edwell T., G. N. Nyakoe, and 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 (February 3, 2019): 1–10. http://dx.doi.org/10.1155/2019/9674054.

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Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers.
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Hajiyev, Chingiz, Demet Cilden-Guler, and Ulviye Hacizade. "Two-Stage Kalman Filter for Fault Tolerant Estimation of Wind Speed and UAV Flight Parameters." Measurement Science Review 20, no. 1 (February 1, 2020): 35–42. http://dx.doi.org/10.2478/msr-2020-0005.

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AbstractIn this study, an estimation algorithm based on a two-stage Kalman filter (TSKF) was developed for wind speed and Unmanned Aerial Vehicle (UAV) motion parameters. In the first stage, the wind speed estimation algorithm is used with the help of the Global Positioning System (GPS) and dynamic pressure measurements. Extended Kalman Filter (EKF) is applied to the system. The state vector is composed of the wind speed components and the pitot scale factor. In the second stage, in order to estimate the state parameters of the UAV, GPS, and Inertial Measurement Unit (IMU) measurements are considered in a Linear Kalman filter. The second stage filter uses the first stage EKF estimates of the wind speed values. Between these two stages, a sensor fault detection algorithm is placed. The sensor fault detection algorithm is based on the first stage EKF innovation process. After detecting the fault on the sensor measurements, the state parameters of the UAV are estimated via robust Kalman filter (RKF) against sensor faults. The robust Kalman filter algorithm, which brings the fault tolerance feature to the filter, secures accurate estimation results in case of a faulty measurement without affecting the remaining good estimation characteristics. In simulations, noise increment and bias type of sensor faults are considered.
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Liu, Zhao, and Anwar Sohel. "Application of MMAE to the Fault Detection of Lithium-Ion Battery." Applied Mechanics and Materials 598 (July 2014): 342–46. http://dx.doi.org/10.4028/www.scientific.net/amm.598.342.

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With the advantage of high energy density, long cycle life and environmental friendliness, Lithium-ion battery has become a promising power source for hybrid and electric vehicles, which are liable to two kinds of failure, overcharge and overdischarge. Because of the capability of detecting multiple faults, Multiple Model Adaptive Estimation (MMAE) method was applied to a model-based fault detection of a lithium-ion battery with a two-order linear electrical model. Parameters that represent normal-mode and faulty-mode of the battery were obtained by a series of experiments, and three Kalman filters were designed thereafter. Finally, simulation verified the performance of the MMAE algorithm on fault detection of these two kinds of fault and it is shown that this technique is able to discern these faults rapidly and accurately.
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Swetapadma, Aleena, and 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.

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Many schemes are reported for shunt fault location estimation, but fault location estimation of series or open conductor faults has not been dealt with so far. The existing numerical relays only detect the open conductor (series) fault and give the indication of the faulty phase(s), but they are unable to locate the series fault. The repair crew needs to patrol the complete line to find the location of series fault. In this paper fuzzy based fault detection/classification and location schemes in time domain are proposed for both series faults, shunt faults, and simultaneous series and shunt faults. The fault simulation studies and fault location algorithm have been developed using Matlab/Simulink. Synchronized phasors of voltage and current signals of both the ends of the line have been used as input to the proposed fuzzy based fault location scheme. Percentage of error in location of series fault is within 1% and shunt fault is 5% for all the tested fault cases. Validation of percentage of error in location estimation is done using Chi square test with both 1% and 5% level of significance.
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Burdusel, Constantin. "A Fault Detection Method for Attitude Sensors of Satellite." Applied Mechanics and Materials 325-326 (June 2013): 769–73. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.769.

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Any system based on data acquisition from different sensors has important characteristics of quality and fiability that are given by the detection and the isolation of the sensors faults. This paper presents two methods of fault detection, applicable in aerospace domain, used in satellite systems, and the simulation of the functionalities of the methods using Matlab. Both methods are based on the estimation of the sensors fault by analysing the differences between the measured value and the estimated one, using an EKF estimator [. Keywords: EKF filter, fault detection, satellite sensors, t-test
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Minh, Vu Trieu, Nitin Afzulpurkar, and 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.

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This paper develops a stochastic hybrid model-based control system that can determine online the optimal control actions, detect faults quickly in the control process, and reconfigure the controller accordingly using interacting multiple-model (IMM) estimator and generalized predictive control (GPC) algorithm. A fault detection and control system consists of two main parts: the first is the fault detector and the second is the controller reconfiguration. This work deals with three main challenging issues: design of fault model set, estimation of stochastic hybrid multiple models, and stochastic model predictive control of hybrid multiple models. For the first issue, we propose a simple scheme for designing faults for discrete and continuous random variables. For the second issue, we consider and select a fast and reliable fault detection system applied to the stochastic hybrid system. Finally, we develop a stochastic GPC algorithm for hybrid multiple-models controller reconfiguration with soft switching signals based on weighted probabilities. Simulations for the proposed system are illustrated and analyzed.
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Nikranjbar, A., M. Ebrahimi, and 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, no. 3 (December 1, 2008): 607–15. http://dx.doi.org/10.1243/09544062jmes1113.

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Much research works address model-free or signal processing and spectral analysis-based fault detection schemes for rotor eccentricity fault in induction motors. Nevertheless, despite existing reliable fault-embedded eccentricity mathematical models such as the winding function method an integrated model-based fault detection algorithm for detecting this fault yet has not been fully explored. This article presents model-based mixed-eccentricity fault detection and diagnosis for induction motors. The proposed algorithm can successfully detect faults and their severity using stator currents. To determine the values of the fault-related parameters, an adaptive synchronization-based parameter estimation algorithm is introduced using particle swarm optimization. Simulation and experiments demonstrate the ability of the algorithm to detect and diagnose these faults. The proposed algorithm can be employed to estimate the parameters, in addition to slowly time varying and abruptly changing parameters.
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Asokan, A., and 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, no. 2 (2007): 133–45. http://dx.doi.org/10.2298/sjee0702133a.

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Fault detection and isolation (FDI) is a task to deduce from observed variable of the system if any component is faulty, to locate the faulty components and also to estimate the fault magnitude present in the system. This paper provides a systematic method of fault diagnosis to detect leak in the three-tank process. The proposed scheme makes use of structured residual approach for detection, isolation and estimation of faults acting on the process [1]. This technique includes residual generation and residual evaluation. A literature review showed that the conventional fault diagnosis methods like the ordinary Chisquare (?2) test method, generalized likelihood ratio test have limitations such as the "false alarm" problem. From the results it is inferred that the proposed FDI scheme diagnoses better when compared to other conventional methods.
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Zhu, Linhai, Jinfu Liu, Yujia Ma, Weixing Zhou, and Daren Yu. "A Coupling Diagnosis Method for Sensor Faults Detection, Isolation and Estimation of Gas Turbine Engines." Energies 13, no. 18 (September 22, 2020): 4976. http://dx.doi.org/10.3390/en13184976.

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In this paper a novel fault detection, isolation, and identification (FDI&E) scheme using a coupling diagnosis method with the integration of the model-based method and unsupervised learning algorithm is proposed and developed for monitoring gas turbine sensor faults, which represents an integration of Square Root Cubature Kalman Filters (SRCKF) and an improved Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm. A detection indicator produced by SRCKF with a specific hypothesis is used for extracting sensor fault features against process and measurement noise, as well as operating conditions. Then, an improved DBSCAN is implemented based on a voting scheme to detect and isolate the faulty sensors. Finally, a residual-based fault estimation scheme is proposed to track sensor fault evolution and help to judge the types of faults. Moreover, the observability of the model involved is analyzed to verify the stable operation of the FDI&E scheme. Various experiments for single and concurrent sensor fault scenarios in a dual-spool gas turbine prototype during a whole flight mission are conducted to demonstrate the effectiveness of the proposed FDI&E scheme. Moreover, comparative studies confirm the superiority of our proposed FDI&E scheme than the existing methods in terms of promptness and robustness of the sensor FDI.
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Dissertations / Theses on the topic "Fault detection/estimation"

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Zhou, Yilun. "Fault detection and distributed estimation with sensor networks." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/61021.

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A sensor network is a distributed system, consisting of plenty of embedded sensors that can be deployed over a large-scale physical environment. One of the major applications of sensor networks is to monitor the state of systems that are evolving in the sensing field. Thanks to the emergence of advancements in high-performance processors, nodes in a sensor network can not only collect measurements but coordinate to estimate the state of systems as well. This thesis proposes a monitoring architecture, where distributed state estimation and fault detection algorithms are implemented by every node in the sensor networks to track the system’s state while simultaneously detecting the faults occurred in either the monitored systems or the sensor networks. Several approaches for different monitoring tasks are presented in this thesis and classified into two main parts: distributed state estimation and fault detection algorithms in the monitoring architecture. In the first part, we present two distributed state estimation algorithms in the sensor networks for the monitoring of a system, which can be described by a centralized, decentralized, or distributed dynamic model. The first one is implemented over a sensor network, where the local estimator in each node consists of a filtering step – which uses a weighted combination of neighboring sensors information – and a model-based state predictor. The filtering weights and prediction parameters jointly minimize both the mean and variance of the prediction error in a Pareto optimization framework at each time step. Since each predictor uses the model of the whole system monitored by the sensor network, the algorithm over a sensor network can only monitor a centralized system or each subsystem of a decentralized system. For a distributed system, where several subsystems interact with each other, the second algorithm implemented over several sensor networks is introduced so that each sensor network can coordinate with neighboring networks to monitor the corresponding subsystem of the distributed system. The second part of the thesis is devoted to fault detection algorithms for process faults in monitored systems and sensor faults in sensor networks. The aforementioned estimation algorithm over a sensor network is applied to design process fault detection algorithm for a centralized or decentralized system. A residual is defined, and suitable stochastic thresholds are designed, allowing to set the parameters so to guarantee an upper bound of false alarms probability. For detecting sensor faults in the sensor networks, the centralized, decentralized, and distributed sensor fault detection schemes are proposed in a discrete-time framework. And the detection performance is compared by an industrial benchmark simulation in a continuous stirred tank heater (CSTH) pilot plant. Then a rigorous fault detectability and detection time interval analysis of the centralized sensor fault detection scheme is presented. The performance of proposed distributed estimation methods and effectiveness of presented fault detection methods are evaluated by extensive numerical and industrial benchmark simulations.
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Stocks, Mikael. "Stator fault detection and parameter estimation in induction machines." Licentiate thesis, Luleå, 2002. http://epubl.luth.se/1402-1757/2002/23.

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Xiong, 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.

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La modélisation des systèmes dynamiques requiert la prise en compte d'incertitudes liées à l'existence inévitable de bruits (bruits de mesure, bruits sur la dynamique), à la méconnaissance de certains phénomènes perturbateurs mais également aux incertitudes sur la valeur des paramètres (spécification de tolérances, phénomène de vieillissement). Alors que certaines de ces incertitudes se prêtent bien à une modélisation de type statistique comme par exemple les bruits de mesure, d'autres se caractérisent mieux par des bornes, sans autre attribut. Dans ce travail de thèse, motivés par les observations ci-dessus, nous traitons le problème de l'intégration d'incertitudes statistiques et à erreurs bornées pour les systèmes linéaires à temps discret. Partant du filtre de Kalman Intervalle (noté IKF) développé dans [Chen 1997], nous proposons des améliorations significatives basées sur des techniques récentes de propagation de contraintes et d'inversion ensembliste qui, contrairement aux mécanismes mis en jeu par l'IKF, permettent d'obtenir un résultat garanti tout en contrôlant le pessimisme de l'analyse par intervalles. Cet algorithme est noté iIKF. Le filtre iIKF a la même structure récursive que le filtre de Kalman classique et délivre un encadrement de tous les estimés optimaux et des matrices de covariance possibles. L'algorithme IKF précédent évite quant à lui le problème de l'inversion des matrices intervalles, ce qui lui vaut de perdre des solutions possibles. Pour l'iIKF, nous proposons une méthode originale garantie pour l'inversion des matrices intervalle qui couple l'algorithme SIVIA (Set Inversion via Interval Analysis) et un ensemble de problèmes de propagation de contraintes. Par ailleurs, plusieurs mécanismes basés sur la propagation de contraintes sont également mis en oeuvre pour limiter l'effet de surestimation due à la propagation d'intervalles dans la structure récursive du filtre. Un algorithme de détection de défauts basé sur iIKF est proposé en mettant en oeuvre une stratégie de boucle semi-fermée qui permet de ne pas réalimenter le filtre avec des mesures corrompues par le défaut dès que celui-ci est détecté. A travers différents exemples, les avantages du filtre iIKF sont exposés et l'efficacité de l'algorithme de détection de défauts est démontré.
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Shafiei, 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.

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In this research work, the combination of three novel approaches is established to estimate the states of three-phase balanced and unbalanced distribution networks and using the developed methods for high impedance fault detection. The effectiveness of the developed methods are proposing a fast real-time state estimator with a low number of measurement devices, avoiding bad data detection in state estimation, and dynamically updating fault current thresholds to detect high impedance faults in the distribution networks.
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Yang, Zaiyue, and 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.

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Yang, 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.

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Su, Jinya. "Fault estimation algorithms : design and verification." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/23231.

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The research in this thesis is undertaken by observing that modern systems are becoming more and more complex and safety-critical due to the increasing requirements on system smartness and autonomy, and as a result health monitoring system needs to be developed to meet the requirements on system safety and reliability. The state-of-the-art approaches to monitoring system status are model based Fault Diagnosis (FD) systems, which can fuse the advantages of system physical modelling and sensors' characteristics. A number of model based FD approaches have been proposed. The conventional residual based approaches by monitoring system output estimation errors, however, may have certain limitations such as complex diagnosis logic for fault isolation, less sensitiveness to system faults and high computation load. More importantly, little attention has been paid to the problem of fault diagnosis system verification which answers the question that under what condition (i.e., level of uncertainties) a fault diagnosis system is valid. To this end, this thesis investigates the design and verification of fault diagnosis algorithms. It first highlights the differences between two popular FD approaches (i.e., residual based and fault estimation based) through a case study. On this basis, a set of uncertainty estimation algorithms are proposed to generate fault estimates according to different specifications after interpreting the FD problem as an uncertainty estimation problem. Then FD algorithm verification and threshold selection are investigated considering that there are always some mismatches between the real plant and the mathematical model used for FD observer design. Reachability analysis is drawn to evaluate the effect of uncertainties and faults such that it can be quantitatively verified under what condition a FD algorithm is valid. First the proposed fault estimation algorithms in this thesis, on the one hand, extend the existing approaches by pooling the available prior information such that performance can be enhanced, and on the other hand relax the existence condition and reduce the computation load by exploiting the reduced order observer structure. Second, the proposed framework for fault diagnosis system verification bridges the gap between academia and industry since on the one hand a given FD algorithm can be verified under what condition it is effective, and on the other hand different FD algorithms can be compared and selected for different application scenarios. It should be highlighted that although the algorithm design and verification are for fault diagnosis systems, they can also be applied for other systems such as disturbance rejection control system among many others.
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Salehpour, 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.

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Zhang, Xiaoxia. "Incipient anomaly detection and estimation for complex system health monitoring." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG025.

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La détection et le diagnostic des défauts naissants pour les systèmes d’ingénierie ou industriels multivariés à bruit élevé sont abordés dans ce travail de thèse par l’intermédiare d’une approche statistique non paramétrique ’globale’.Un défaut naissant induit un changement anormal dans les valeurs mesurées de la variable du système. Cependant, un tel changement est faible, et tend à ne pas causer de changements évidents dans les paramètres des distributions des signaux du système. En particulier dans un environnement bruité, les caractéristiques de ces défaults faible peuvent être masquées par le bruit et rend celui-ci difficile à évaluer. Dans une telle situation, l’utilisation de méthodes paramétriques traditionnelles pour la détection échouent. Pour faire face à ces difficultés et effectuer la détection et le diagnostic des défauts, une approche’globale’ qui peut prendre en compte la signature totale des défauts est nécessaire. La détection de défauts naissants peut être obtenue par la mesure des différences entre les distributions avant et après l’apparition du défaut. Certaines méthodes basées sur la distribution (dites ’globales’) ont été proposées, mais les performances de détection de ces approches existantes dans un environnement à haut niveau de bruit devraient être améliorées. Dans ce contexte, la divergence de Jensen-Shannon est considérée comme un indicateur de défaut ’global’ pour effectuer la détection et le diagnostic de défaut naissant dans un environnement à haut niveau de bruit. Ses performances de détection pour de petites variations anormales noyées dans le bruit sont validés en simulation. En outre, le problème de l’estimation des défauts est également étudié dans ce travail. Un modèle théorique d’estimation de la sévérité des défauts à parti dépend de la valeur de la divergence pour des conditions Gaussiennes est établi. La précision du modèle d’estimation est évaluée sur des modèles numériques par le biais de simulations. Ensuite, l’approche statistique ’globale’ est mise en oeuvre pour à deux applications dans le domaine de l’ingénierie. La première concerne la détection de fissures naissantes dans un matériau conducteur. La divergence de Jensen-Shannon combinée à l’analyse en composantes indépendantes et à la décomposition on ondelettes a été appliquée à la détection et à la caractérisation de fissures mineures dans des structures conductrices avec des perturbations bruit sur la base de signaux d’impédance expérimentaux. La deuxième application concerne le diagnostic de défauts naissants dans un processus non linéaire multivarié avec un bruit élevé. Le ’Tennessee Eastman Process’ (TEP) est un processus non linéaire multivarié typique pour lequel nous avons appliqué, la divergence de Jensen-Shannon combinée à l’analyse en composantes principales à noyau (ACPN) est pour étudier la détection de défauts naissants dont les difficultés de sont largement décrites dans la littérature
Incipient 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
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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.

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This 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.

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Books on the topic "Fault detection/estimation"

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Anton, Stoorvogel, and Sannuti Peddapullaiah 1941-, eds. Filtering theory: With applications to fault detection, isolation, and estimation. Boston ; Berlin: Birkhäuser, 2007.

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C, Merrill Walter, Duyar Ahmet, and 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.

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C, Merrill Walter, Duyar Ahmet, and 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.

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Neural network-based state estimation of nonlinear systems: Application to fault detection and isolation. New York: Springer, 2010.

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Lehrasab, Nadeem. A generic fault detection and isolation approach for single-throw mechanical equipment. Birmingham: University of Birmingham, 1999.

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Stoorvogel, Anton A., Peddapullaiah Sannuti, and Ali Saberi. Filtering Theory: With Applications to Fault Detection, Isolation, and Estimation. Springer London, Limited, 2007.

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A distributed fault-detection and diagnosis system using on-line parameter estimation. [Washington, D.C: National Aeronautics and Space Administration, 1991.

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Book chapters on the topic "Fault detection/estimation"

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Mahmoud, Magdi S. "Fuzzy Fault Detection and Control." In Fuzzy Control, Estimation and Diagnosis, 483–546. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54954-5_9.

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Ding, Steven X. "Basic Requirements on Fault Detection and Estimation." In 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.

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Isermann, Rolf. "Fault detection with state observers and state estimation." In Fault-Diagnosis Systems, 231–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-30368-5_11.

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Ding, Steven X. "Basic Methods for Fault Detection and Estimation in Static Processes." In 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.

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Wei, M., D. Lapucha, and H. Martell. "Fault Detection and Estimation in Dynamic Systems." In 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.

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Giua, Alessandro. "State Estimation and Fault Detection Using Petri Nets." In 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.

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Talebi, Heidar A., Farzaneh Abdollahi, Rajni V. Patel, and Khashayar Khorasani. "A Robust Actuator Gain Fault Detection and Isolation Scheme." In 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.

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Liu, Yang, Zidong Wang, and Donghua Zhou. "Filtering and Fault Detection for Nonlinear Systems with Polynomial Approximation." In State Estimation and Fault Diagnosis under Imperfect Measurements, 89–114. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003309482-6.

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Liu, Zhigang. "Slide Plate Fault Detection of Pantograph Based on Image Processing." In 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.

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Isermann, Rolf. "Experiences with Process Fault Detection Methods via Parameter Estimation." In 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.

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Conference papers on the topic "Fault detection/estimation"

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Zhang, Xiaodong, Remus C. Avram, Liang Tang, and Michael J. Roemer. "A Unified Nonlinear Approach to Fault Diagnosis of Aircraft Engines." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-95803.

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Many existing aircraft engine diagnostic methods are based on linearized engine models. However, the dynamics of aircraft engines are highly nonlinear and rapidly changing. Future engine health management designs will benefit from new methods that are directly based on intrinsic nonlinearities of the engine dynamics. In this paper, a fault detection and isolation (FDI) method is developed for aircraft engines by utilizing nonlinear adaptive estimation and nonlinear observer techniques. Engine sensor faults, actuator faults and component faults are considered under one unified nonlinear framework. The fault diagnosis architecture consists of a fault detection estimator and a bank of nonlinear fault isolation estimators. The fault detection estimator is used for detecting the occurrence of a fault, while the bank of fault isolation estimators is employed to determine the particular fault type or location after fault detection. Each isolation estimator is designed based on the functional structure of a particular fault type under consideration. Specifically, adaptive estimation techniques are used for designing the isolation estimators for engine component faults and actuator faults, while nonlinear observer techniques are used for designing the isolation estimators for sensor faults. The FDI architecture has been integrated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) engine model developed by NASA researchers in recent years. The engine model is a realistic representation of the nonlinear aero thermal dynamics of a 90,000-pound thrust class turbofan engine with high-bypass ratio and a two-spool configuration. Representative simulation results and comparative studies are shown to verify the effectiveness of the nonlinear FDI method.
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Xin Wang, Shuli Sun, and Ying Shi. "Fault detection and noise variance identifier with cooperation fault-torlerance for multisensor system." In 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280148.

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Gadsden, S. A., and S. R. Habibi. "State Estimation and Fault Detection of an Electrohydrostatic Actuator." In ASME/BATH 2014 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/fpmc2014-7847.

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The electrohydrostatic actuator (EHA) is an efficient type of linear actuator commonly found in aerospace applications. It consists of an external gear pump (fluid), an electric motor, a closed hydraulic circuit, a number of control valves and ports, and a linear actuator. An EHA, built for experimentation, is studied in this paper. Two types of estimation strategies, the popular Kalman filter (KF) and the smooth variable structure filter (SVSF), are applied to the EHA for kinematic state and parameter estimation. The KF strategy yields the statistical optimal solution to linear estimation problems. However, the KF becomes unstable when strict assumptions are violated. The SVSF is an estimation strategy based on sliding mode concepts, which brings an inherent amount of stability to the estimation process. Recent advances in SVSF theory include a time-varying smoothing boundary layer. This method, known as the SVSF-VBL, offers an optimal formulation of the SVSF as well as a method for detecting changes or faults in a system. In addition to the application of the KF and SVSF for state estimation, the SVSF-VBL is applied to the EHA for the purposes of fault detection. The EHA is operated under various operating conditions (normal, friction fault, leakage fault, and so on), and the experimental results are presented and discussed.
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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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Chunxia Wang, Chenglin Wen, and Yang Lu. "A fault diagnosis method by using extreme learning machine." In 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280215.

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Noel, Nana K., Kari Tammi, Gregory D. Buckner, and Nathan S. Gibson. "Intelligent Kalman Filtering for Fault Detection on an Active Magnetic Bearing System." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2122.

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One of the challenges of condition monitoring and fault detection is to develop techniques that are sufficiently sensitive to faults without triggering false alarms. In this paper we develop and experimentally demonstrate an intelligent approach for detecting faults in a single-input, single-output active magnetic bearing. This technique uses an augmented linear model of the plant dynamics together with a Kalman filter to estimate fault states. A neural network is introduced to enhance the estimation accuracy and eliminate false alarms. This approach is validated experimentally for two types of fabricated faults: changes in suspended mass and coil resistance. The Kalman filter alone is shown to be incapable of identifying all fault cases due to modeling uncertainties. When an artificial neural network is trained to compensate for these uncertainties, however, all fault conditions are identified uniquely.
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Garimella, Phanindra, and Bin Yao. "Fault Detection of an Electro-Hydraulic Cylinder Using Adaptive Robust Observers." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-61718.

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This paper presents the application of an Adaptive Robust Observer (ARO) to the detection of some common faults that occur in hydraulic cylinder drive units such as the lack of sufficient supply pressure, reduced hydraulic compliance and excessive leakage of the hydraulic fluid. All of these faults could contribute to the reduced performance of the system and eventual complete failure. The inherent nonlinear system dynamics, severe parametric uncertainties and model uncertainties make fault detection in hydraulic systems difficult to implement in practice. To tackle these problems, the Adaptive Robust Observer presented in this paper is designed using the nonlinear system dynamics and robust filter structures which attenuate the effect of model uncertainties to give robust estimates of the states. By using on-line parameter adaptation the accuracy of the state-estimate is improved. Also, by estimating the parameters only when certain persistence of excitation conditions are satisfied, bounds on parameter estimation errors can be computed which would help in setting better threshold limits on the residual signals which improves the robustness of the fault detection scheme. Simulation and experimental results on the swing-arm of a three-degree of freedom hydraulic robot arm are presented to demonstrate the effectiveness of the proposed fault detection scheme.
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Kobayashi, Takahisa, and Donald L. Simon. "Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38550.

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In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.
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Tian, Kun, and Hai-hua Yu. "Robust non-fragile fault-tolerant H∞ control for time-delay uncertain linear systems." In 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280214.

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Tornqvist, David, Saikat Saha, and Fredrik Gustafsson. "Fault detection using nonlinear parameter estimation." In 2011 IEEE Aerospace Conference. IEEE, 2011. http://dx.doi.org/10.1109/aero.2011.5747438.

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Reports on the topic "Fault detection/estimation"

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Jenkins, Cody David. Bearing Fault Detection and Wear Estimation Using Machine Learning. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1557163.

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