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Auswahl der wissenschaftlichen Literatur zum Thema „Faulty variable isolation“
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Zeitschriftenartikel zum Thema "Faulty variable isolation"
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Asokan, A., und 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, Nr. 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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Sellami, T., H. Berriri, S. Jelassi, A. M. Darcherif und M. F. Mimouni. „Sliding Mode Observers-based Fault Detection and Isolation for Wind Turbine-driven Induction Generator“. International Journal of Power Electronics and Drive Systems (IJPEDS) 8, Nr. 3 (01.09.2017): 1345. http://dx.doi.org/10.11591/ijpeds.v8.i3.pp1345-1358.
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Inter-turn short-circuit (ITSC) faults on the induction machine has received much attention in the recent years. Early detection of such faults in wind turbine systems would allow to avoid fluctuation on wind power output and maintain the reliability level. In this paper, Sliding Mode Observers (SMO)-based fault detection and isolation method is developed for induction generator (IG)-based variable-speed grid-connected wind turbines. Firstly, the dynamic model of the wind turbine and IG was given and then, the control was made based on Maximum Power Point Tracking (MPPT) method. The IG closed-loop via Indirect Rotor Flux Oriented Control (IRFOC) scheme was also described. Hence, the performance of the wind turbine system and the stability of injected power to the grid were analyzed under the ITSC fault conditions. The control schemes were proved to be inherently unstable under the faulty conditions. Then, robust SMO were investigated to design an ITSC fault detection and isolation scheme. Finally, simulation results of ITSC detection and isolation in the variable-speed grid-connected wind turbine with affected IG confirm the theoretical development.
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Zhao, Chunhui, und Wei Wang. „Efficient faulty variable selection and parsimonious reconstruction modelling for fault isolation“. Journal of Process Control 38 (Februar 2016): 31–41. http://dx.doi.org/10.1016/j.jprocont.2015.12.002.
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Antory, D., U. Kruger, G. Irwin und G. McCullough. „Fault diagnosis in internal combustion engines using non-linear multivariate statistics“. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 219, Nr. 4 (01.06.2005): 243–58. http://dx.doi.org/10.1243/095965105x9614.
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This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce non-linear relationships between the recorded engine variables, the paper proposes the use of non-linear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new non-linear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady state operating conditions. More precisely, non-linear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (a) an enhanced identification of potential root causes of abnormal events and (b) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA-based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, while the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 litre diesel engine mounted on an experimental engine test bench facility.
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Kariwala, Vinay, Pabara-Ebiere Odiowei, Yi Cao und Tao Chen. „A branch and bound method for isolation of faulty variables through missing variable analysis“. Journal of Process Control 20, Nr. 10 (Dezember 2010): 1198–206. http://dx.doi.org/10.1016/j.jprocont.2010.07.007.
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Nie, Lei, Yizhu Ren, Rouhui Wu und Mengying Tan. „Sensor Fault Diagnosis, Isolation, and Accommodation for Heating, Ventilating, and Air Conditioning Systems Based on Soft Sensor“. Actuators 12, Nr. 10 (17.10.2023): 389. http://dx.doi.org/10.3390/act12100389.
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Heating, Ventilating, and Air Conditioning (HVAC) systems often suffer from unscheduled maintenance or abnormal shutdown due to the fault of their interior sensor system. Traditional fault diagnosis methods for HVAC sensor systems primarily focus on sensor fault diagnosis and isolation, lacking fault accommodation. Therefore, to realize effective sensor fault detection, identification, and accommodation (SFDIA), a method for HVAC SFDIA based on the soft sensor is proposed. First, a diagnosis soft sensor with multi-variable input is constructed to estimate the output of the physical sensor being diagnosed. The residual between the estimated value of the diagnosis soft sensor and the measurement of the physical sensor is used as an indicator of the sensor’s condition. If the residual exceeds the fault threshold, the sensor is diagnosed to be faulty. In order to maintain valid sensor output, an accommodation soft sensor is constructed using the historical normal value. The erroneous output of the faulty sensor is substituted by the estimated value from the accommodation soft sensor, thereby realizing sensor fault tolerance control. Experimental results demonstrate that the average false alarm rate for sensor fault diagnosis is 1.57% and the average fault diagnosis rate is 96.51%. The predictive mean absolute error (MAE) and root-mean-square error (RMSE) of the recovered soft sensors are 0.0525 and 0.0738, respectively. Thus, the soft sensors developed in this paper exhibit satisfying ability in HVAC SFDIA.
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Hu, Yunyun, Yue Wang und Chunhui Zhao. „A sparse fault degradation oriented fisher discriminant analysis (FDFDA) algorithm for faulty variable isolation and its industrial application“. Control Engineering Practice 90 (September 2019): 311–20. http://dx.doi.org/10.1016/j.conengprac.2019.07.007.
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Sorribes Pamer, Felix, Bernd Luber, Josef Fuchs, Thomas Kern und Martin Rosenberger. „Data-driven fault diagnosis of bogie suspension components with on- board acoustic sensors“. PHM Society European Conference 5, Nr. 1 (22.07.2020): 13. http://dx.doi.org/10.36001/phme.2020.v5i1.1211.
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This paper proposes a data-driven approach for fault-detection and isolation of bogie suspension components with on-board acoustic sensors. The fault detection technique is based on the acoustic emissions variation due to structural modal coupling changes in the presence of faulty components. A suspensions component failure introduces an imbalance into the system, resulting in dynamics interferences between the motions. These interferences modify the energy introduced into the system as well as its acoustic emissions. The unknown arbitrary track irregularities generate together with a variable train speed a random nonstationary vehicle excitation. Speech recognition techniques were used to generate features that consider this phenomenon. Frequency spectrums were analysed in different operating conditions to design efficient features. The robustness of the methodology was verified with data from two different test measurement campaigns on a test ring, where the influence of the sensor locations for the fault classification process was studied. The proposed methodology achieved good fault classification performance on the investigated use cases, removed dampers and 50% damper degradation on primary and secondary vertical suspension.
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Abbas, Mohammed, Houcine Chafouk und Sid Ahmed El Mehdi Ardjoun. „Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach“. Sensors 24, Nr. 3 (23.01.2024): 728. http://dx.doi.org/10.3390/s24030728.
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Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period.
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Uddin, Md Aftab, Mst Aysha Siddiqua und Mst Sadia Ahmed. „Isolation and quantification of indicator and pathogenic microorganisms along with their drug resistance traits from bottled and jar water samples within Dhaka city, Bangladesh“. Stamford Journal of Microbiology 9, Nr. 1 (27.02.2020): 12–14. http://dx.doi.org/10.3329/sjm.v9i1.45651.
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Commercial drinking water may serve as potential threat to public health if these items are contaminated with a number of pathogenic microorganisms due to faulty manufacturing process. Present study attempted to isolate and quantify the microorganisms from various jar and bottle water samples collected from various areas of Dhaka city. Antibiotic susceptibility pattern of suspected bacterial isolates were also determined in this study. Out of the eighteen samples studied, ten were jar water samples and eight were bottled water samples. The range of total viable bacterial count (TVBC) in these samples ranged from 102 to 105 cfu/ml. Specific pathogens such as, Salmonella spp., Shigella spp., Vibrio spp. and fecal coliforms could not be found in these samples. However coliforms could be detected in 10 samples. The antibiogram study showed that all Escherichia coli and Klebsiella spp. isolates found from these samples were sensitive against gentamicin (10 μg) and azithromycin (30 μg). Variable antibiotic resistance among these bacterial isolates was detected against cefotaxime (30 μg), streptomycin (10 μg) and erythromycin (15 μg).
Stamford Journal of Microbiology, Vol.9(1) 2019: 12-14
Dissertationen zum Thema "Faulty variable isolation"
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Yang, Junjie. „Fault Diagnosis and Prognosis in multivariate complex systems“. Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPAST001.
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Le diagnostic et le pronostic des défaillances ont suscité une attention considérable dans l'industrie et le monde universitaire en raison des exigences croissantes en matière de fiabilité, de disponibilité, de maintenabilité et de sécurité.Malgré les progrès significatifs, les méthodologies de diagnostic de défauts existantes souffrent toujours de problèmes, tels que le manque de données défectueuses suffisantes pour la formation, l'inefficacité des données distribuées complexes, la faible sensibilité aux défauts naissants et l'interférence du bruit et des valeurs aberrantes.Par conséquent, ce travail propose une nouvelle méthode de classification à une classe mise en œuvre en générant des ancres et en sélectionnant la marge de la région pour déterminer une région saine comme zone de décision.Ensuite, une mesure de distance particulière appelée distance de Mahalanobis locale est définie pour indiquer la distance entre un échantillon et la région saine.Sur la base de la méthode de classification à une classe proposée et de l'indice LMD, ce travail développe d'abord une approche de détection des défauts naissants en combinant l'indice LMD et la technique de somme cumulative de densité de probabilité empirique.Ce travail examine également l'efficacité de l'indice LMD en tant que caractéristique représentative pour la détection des défauts.Deuxièmement, ce travail propose une méthode d'isolation de la variable défectueuse pour les cas de défaut unique en combinant la technique LMD avec l'idée du diagramme de contribution.Troisièmement, une expression analytique du taux d'augmentation des défauts est dérivée de l'indice LMD pour la tâche d'estimation de la gravité des défauts.Enfin, nous développons une nouvelle approche basée sur la reconstruction en utilisant la distance de Mahalanobis locale comme indice de détection pour améliorer les performances d'isolation et d'estimation.La méthode améliorée peut isoler avec précision plusieurs variables défectueuses et estimer simultanément l'amplitude de leurs défauts.L'étude de cas basée sur les données de processus du réacteur à réservoir agité à flux continu montre que la technique LMD présente des avantages significatifs pour le problème de diagnostic des défauts, tels qu'une sensibilité élevée aux défauts naissants, une robustesse au bruit et aux valeurs aberrantes, et l'absence d'hypothèse de distribution.Les méthodes de diagnostic de défauts développées sur la base de la technique LMD sont nettement plus performantes que les solutions les plus récentes.L'étude comparative sur les données de roulement de la Case Western Reserve University indique que la technique LMD peut être utilisée comme approche d'extraction de caractéristiques et qu'elle est plus efficace et plus robuste que les autres techniques statistiquesFault diagnosis and prognosis have attracted huge attention in industry and academia for the increasing requirements on reliability, availability, maintainability, and safety.Despite the significant progress, the existing fault diagnosis methodologies still suffer from challenges, such as the lack of sufficient faulty data for training, ineffectiveness to complex distributed data, low sensitivity to incipient faults, and the interference of noise and outliers.Therefore, this work proposes a new one-class classification method implemented by generating anchors and selecting the region margin to determine a healthy region as a decision area.Then a particular distance measurement called local Mahalanobis distance is then defined to indicate the distance between a sample and the healthy region.Based on the proposed one-class classification method and the LMD index, this work first develops an incipient fault detection approach by combining the LMD index and the empirical probability density cumulative sum technique.This work also discusses the efficiency of LMD as a representative feature for fault detection.Secondly, this work proposes the faulty variable isolation method for single fault cases by combining the LMD technique with the contribution plot idea.Thirdly, an analytical expression of fault increasing rate is derived from the LMD index for the fault severity estimation task.Finally, we further develop a new reconstruction-based approach using the local Mahalanobis distance as a detection index to improve the isolation and estimation performance.The improved method can accurately isolate multiple faulty variables and estimate their fault amplitudes simultaneously.The case study based on the Continuous-flow Stirred Tank Reactor process data shows that the LMD technique has significant benefits for the fault diagnosis problem, such as high sensitivity to incipient faults, robustness to noise and outliers, and no distribution assumption.The fault diagnosis methods developed on LMD significantly outperform state-of-the-art solutions.The comparative study on the Case Western Reserve University bearing data indicates that the LMD technique can be used as a feature extraction approach and is more effective and robust than the other statistical techniques
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Mnassri, Baligh. „Analyse de données multivariées et surveillance des processus industriels par analyse en composantes principales“. Phd thesis, Aix-Marseille Université, 2012. http://tel.archives-ouvertes.fr/tel-00749282.
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Ce mémoire de thèse présente une étude fondamentale enrichie par des contributions qui sont articulées autour de la modélisation de processus ainsi qu'un diagnostic de défauts en utilisant l'analyse en composantes principales (ACP). Dans l'objectif d'un choix optimal du modèle ACP, une étude comparative de quelques critères connus dans la littérature nous a permis de conclure que le problème rencontré est souvent lié à une ignorance des variables indépendantes et quasi-indépendantes. Dans ce cadre, nous avons réalisé deux démonstrations mettant en évidence les limitations de deux critères en particulier la variance non reconstruite (VNR). En s'appuyant sur le principe d'une telle variance, nous avons proposé trois nouveaux critères. Parmi eux, deux ont été considérés comme étant empiriques car seule l'expérience permettra de prouver leur efficacité. Le troisième critère noté VNRVI représente un remède à la limitation du critère VNR. Une étude de sa consistance théorique a permis d'établir les conditions garantissant l'optimalité de son choix. Les résultats de simulation ont validé une telle théorie en prouvant ainsi que le critère VNRVI étant plus efficace que ceux étudiés dans cette thèse.
Dans le cadre d'un diagnostic de défauts par ACP, l'approche de reconstruction des indices de détection ainsi que celle des contributions ont été utilisées. A travers une étude de généralisation, nous avons étendu le concept d'isolabilité de défauts par reconstruction à tout indice quadratique. Une telle généralisation nous a permis d'élaborer une analyse théorique d'isolabilité de défauts par reconstruction de la distance combinée versus celles des indices SPE et T2 de Hotelling en mettant en avant l'avantage de l'utilisation d'une telle distance. D'autre part, nous avons proposé une nouvelle méthode de contribution par décomposition partielle de l'indice SPE. Cette approche garantit un diagnostic correct de défauts simples ayant de grandes amplitudes. Nous avons également étendu une méthode de contribution classiquement connue par la RBC au cas multidimensionnel. Ainsi, la nouvelle forme garantit un diagnostic correct de défauts multiples de grandes amplitudes. En considérant la complexité de défauts, nous avons exploité la nouvelle approche de contribution RBC afin de proposer une nouvelle qui s'appelle RBCr. Cette dernière s'appuie sur un seuil de tolérance pour l'isolation de défauts. Une analyse de diagnosticabilité basée sur la RBCr montre que celle-ci garantit l'identification des défauts détectables. Ces derniers sont garantis isolables si leurs amplitudes satisfont les mêmes conditions d'isolabilité établies pour l'approche de reconstruction des indices.
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Wang, Jian, und 王健. „Experiment and Analysis of Near-Fault Seismic Isolation Using Sliding Bearings with Variable Curvatures“. Thesis, 2006. http://ndltd.ncl.edu.tw/handle/79018104044579564168.
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碩士國立高雄第一科技大學
營建工程所
94
ABSTRACT Conventional sliding isolation systems (e.g. Friction Pendulum System， FPS)may not be effective when the isolated structures are subjected to near-fault ground motions. The reason is that the isolation periods commonly adopted in conventional sliding isolators are usually in the range of the pulse periods of near-fault earthquakes. As a result, it may lead to a resonant motion that can reduce the effectiveness and safty of isolation. In order to improve the performance of near-fault isolation, an innovative isolator names “Polynomial Friction Pendulum Isolator” (PFPI) is proposed in this study. The restoring stiffness of this new type of isolators possesses a softening and a hardening section. By reducing the restoring stiffness in the softening section the structural acceleration can be reduced. On the other hand, by increasing the restoring stiffness in the hardening section, the large isolator drift induced by near-fault ground motion can be suppressed. Both theoretical and experimental studies were carried out in this work. The theoretical study includes：(1) Derivation of a formula that describes the hysteretic behavior of PFPI. (2) Parametric study on the optimal design III parameters of PFPI for engineering application. (3) Comparison of isolation performance of PFPI with those of other sliding isolators. In the experiment study, two tasks have been accomplished: (1) PFPI isolators were fabricated and a cyclic element test was conducted. (2) A shaking table test for a structure with PFPI was also conducted. Both test result were verified by the theoretical data. The result of theoretical study has shown that when subjected to a long-period pulse-like ground motion, the proposed isolator effectively suppresses the isolator drift without increasing the structural acceleration. The experimental data verified the feasibility of isolation technology using PFPI isolators, and have also verified that the dynamic behavior of the isolators is predictable by the theoretical formula.
Buchteile zum Thema "Faulty variable isolation"
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Fezai, Radhia, Okba Taouali, Majdi Mansouri, Mohamed Faouzi Harkat und Nasreddine Bouguila. „Sensor Fault Detection and Isolation Based on Variable Moving Window KPCA“. In Studies in Systems, Decision and Control, 31–54. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1746-4_2.
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Tian, Huifeng, und Li Jia. „Dynamic Process Fault Isolation and Diagnosis Using Improved Fisher Discriminant Analysis and Relative Error of Variance“. In Communications in Computer and Information Science, 201–11. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6373-2_21.
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Liu, Jialin, und Ding-Sou Chen. „Multiple Sensor Fault Isolation Using Contribution Plots without Smearing Effect to Non-Faulty Variables“. In Computer Aided Chemical Engineering, 1517–21. Elsevier, 2012. http://dx.doi.org/10.1016/b978-0-444-59506-5.50134-6.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Faulty variable isolation"
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Yu, Jungwon, Jonggeun Kim, Hansoo Lee, Seunghwan Jung, June Ho Park und Sungshin Kim. „Application of CART-Based Variable Ranking for Faulty Variable Isolation in Tennessee Eastman Benchmark Process“. In 2019 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2019. http://dx.doi.org/10.1109/icit.2019.8755167.
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Tang, Liang, Allan J. Volponi und Ethan Prihar. „Extending Engine Gas Path Analysis Using Full Flight Data“. In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90161.
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Abstract The ability to trend engine module performance and provide engine system fault detection and isolation are arguably, core capabilities for any engine Condition Based Maintenance (CBM) system. The origins of on-condition monitoring can be traced back nearly four decades, and a methodology known as Gas Path Analysis (GPA) has played a pivotal role in its evolution. Legacy Gas Path Analysis is a general methodology that assesses and quantifies changes in the underlying performance of the major modules of the engine (compressors and turbines), which in turn, directly affect overall performance measures of interest such as fuel consumption, power availability, compressor surge margins, and the like. Additionally, this approach is easily adapted to enable fault detection and identification of many engine system accessory faults such as variable stator vanes, handling and customer bleeds, sensor biases, and drift. Classical GPA has been confined to off-board analysis of averaged snapshot data when the engine is in steady state operation. This discrete data point approach, while reasonably accurate and repeatable, introduces a time latency to confidently detect and identify a faulty condition. Depending on the type and severity of the underlying fault, time to identify can be the differentiating factor in avoiding an unanticipated engine removal, flight delay or cancellation, in-flight engine shutdown, or catastrophic event. In this paper, we explore the use of streaming full flight data, which includes both transient and steady state operation. This type of data stream, when properly processed by gas path analysis and information fusion algorithms, allows faster anomaly detection, credible fault persistency checks, and timely fault identification within the current flight.
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Koh, Christopher K. H., Jianjun Shi, William J. Williams und Jun Ni. „Detection and Isolation of Faults in the Stamping Process“. In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0833.
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Abstract The sheet metal drawing operation is a complex manufacturing process involving more than forty process variables. A fault is said to occur when any of these process variables deviate beyond their specified limits. Current detection schemes based on this principle have exceptionally high false alarms rates. They are also unable to detect frequency or phase shifted fault signals whose amplitudes remain within specifications; nor do they provide information about the multiplicity1 or location2 of the faults. A method of overcoming these limitations is proposed in this paper. It is based on the orthogonal Haar transform which is used to generate a set of detection signals for the different fault conditions.
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DePold, Hans R., Ravi Rajamani, William H. Morrison und Krishna R. Pattipati. „A Unified Metric for Fault Detection and Isolation in Engines“. In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-91095.
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In this paper we make two key contributions. First, we formalize the effectiveness of fault detection and isolation (FDI) with a metric that globally considers the following: variance in engine parameter estimate residuals under normal conditions, costs of missed detections and false alarms, costs associated with misclassification of faults, fault frequencies and fault severities. Reducing the error variance increases the signal-to-noise ratio, thereby increasing the reliability and speed of fault-detection algorithms. Minimizing missed detections has enormous implications on operational safety, while minimizing false alarms and fault misclassifications has implications on downtime, asset management, cannot duplicates, and operational costs. This metric measures the trade off between reducing data error variances, between false and missed detects, and misclassification of faults. As a second contribution, we embed this metric in a systematic data-driven diagnostic optimization process for normative decisions on input parameter selection for residual generation, FDI methods and prediction/classification fusion techniques.
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Tsai, C. S., Tsu-Cheng Chiang und Bo-Jen Chen. „Finite Element Formulations and Theoretical Study for VCFPS“. In ASME 2003 Pressure Vessels and Piping Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/pvp2003-2117.
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The friction pendulum system (FPS), a type of base isolation technology, has been recognized as a very efficient tool for controlling the seismic response of a structure during an earthquake. However, previous studies have focused mainly on the seismic behavior of base-isolated structures far from active earthquake faults. In recent years, there have been significant studies on the efficiency of the base isolator when subjected to near-fault ground motions. It is suggested from these studies that the long-duration pulse of near-fault ground motions results in significant response of a base-isolated structure. In view of this, an advanced base isolator called the variable curvature friction pendulum system (VCFPS) is proposed in this study. The radius of the curvature of VCFPS is lengthened with an increasing of the isolator displacement. Therefore, the fundamental period of the base-isolated structure can be shifted further away from the predominant period of near-fault ground motions. Finite element formulations for VCFPS have also been proposed in this study. The numerical results show that the base shear force and story drift of the superstructure during near-fault ground motion can be controlled within a desirable range with the installation of VCFPS. Therefore, the VCFPS can be adopted for upgrading the seismic resistance of the structures adjacent to an active fault.
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Ganguli, Ranjan, Rajeev Verma und Niranjan Roy. „Soft Computing Application for Gas Path Fault Isolation“. In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53209.
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A fuzzy system that automatically develops its rule base from a linearized performance model of the engine by selecting the membership functions and number of fuzzy sets is developed in this study to perform gas turbine fault isolation. The faults modeled are module faults in five modules: fan, low pressure compressor, high pressure compressor, high pressure turbine and low pressure turbine. The measurements used are deviations in exhaust gas temperature, low rotor speed, high rotor speed and fuel flow from a base line ‘good engine’. A genetic algorithm is used to tune the fuzzy sets to maximize fault isolation success rate. A novel scheme is developed which optimizes the fuzzy system using very few design variables and therefore is computationally efficient. Results with simulated data show that genetic fuzzy system isolates faults with accuracy greater than that of a manually developed fuzzy system developed by the authors. Furthermore, the genetic fuzzy system allows rapid development of the rule base if the fault signatures and measurement uncertainties change. In addition, the genetic fuzzy system reduces the human effort needed in the trial and error process used to design the fuzzy system and makes the development of such a system easier and faster. A radial basis neural network is also used to preprocess the measurements before fault isolation. The radial basis network shows significant noise reduction and when combined with the genetic fuzzy system leads to a diagnostic system that is highly robust to the presence of noise in data.
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Borguet, S., und O. Le´onard. „Constrained Sparse Estimation for Improved Fault Isolation“. In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-45711.
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Least-squares-based methods are very popular in the jet engine community for health monitoring purpose. Their isolation capability can be improved by using a prior knowledge on the health parameters that better matches the expected pattern of the solution i.e., a sparse one as accidental faults impact at most one or two component(s) simultaneously. On the other hand, complimentary information about the feasible values of the health parameters can be derived in the form of constraints. The present contribution investigates the effect of the addition of such constraints on the performance of the sparse estimation tool. Due to its quadratic programming formulation, the constraints are integrated in a straightforward manner. Results obtained on a variety of fault conditions simulated with a commercial turbofan model show that the inclusion of constraints further enhance the isolation capability of the sparse estimator. In particular, the constraints help resolve a confusion issue between high pressure compressor and variable stator vanes faults.
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Liu, Jialin, David Shan Hill Wong und Ding-Sou Chen. „Isolating faulty variables for fault propagation using Bayesian decision theory“. In 2013 European Control Conference (ECC). IEEE, 2013. http://dx.doi.org/10.23919/ecc.2013.6669296.
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Loboda, Igor, Juan Luis Pérez-Ruiz, Sergiy Yepifanov und Roman Zelenskyi. „Comparative Analysis of Two Gas Turbine Diagnosis Approaches“. In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91644.
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Abstract Gas turbine diagnostics that relies on gas path measurements is a well-developed area with many algorithms developed. They follow two general approaches, data-driven, and physics-based. The first approach uses deviations of monitored variables from their baseline values. A diagnostic decision is traditionally made in the space of these deviations (diagnostic features) by pattern recognition techniques, for example, artificial neural networks. The necessary fault classes can be constructed from deviation vectors (patterns) using the displays of real faults, and the approach has a theoretical possibility to exclude a complex physics-based model and its inherent errors from a diagnostic process. For the second approach known as a gas path analysis, a nonlinear physics-based model (a.k.a. thermodynamic model) is an integral part of a diagnostic process. The thermodynamic model (or the corresponding linear model) relates monitored variables with operational conditions and model’s internal quantities called fault parameters. The identification of the thermodynamic model on the basis of known measurements of the monitored variables and operational conditions allows estimating unknown fault parameters. The knowledge of these parameters drastically simplifies a final diagnostic decision because great values of these parameters indicate damaged engine components and give us the measure of damage severity. As the diagnostic decision seems to be simple, the studies following this approach are usually completed by the analysis of fault parameter estimation accuracy, and complex pattern recognition techniques are not employed. Instead, simple tolerance-based fault detection and isolation is sometimes performed. It is not clear from known comparative studies which of the two approaches is more accurate, and the issue of seems to be challenging. This paper tries to solve this problem, being grounded on the following principles. We consider that a key difference of the second approach is a transformation from the diagnostic space of the deviations of monitored variables to the space of fault parameters. To evaluate the influence of this transformation on diagnostic accuracy, the other steps of the approaches should be equal. To this end, the pattern recognition technique employed in the data-driven approach is also included in the physics-based approach where it is applied to recognize fault parameter patterns instead of a tolerance-based rule. To realize and compare the data-driven and modified physics-based approaches, two corresponding diagnostic procedures differing only by the mentioned transformation have been developed. They use the same set of deviation vectors of healthy and faulty engines as input data and finally compute true classification rates that are employed to compare the procedures. The results obtained for different cases of the present comparative study show that the classification rates are practically the same for these procedures, and this is true for both fault detection and fault isolation. That is, correct classification does not depend on the mentioned transformation, and both approaches are equal from the standpoint of the classification accuracy of engine states.
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Scacchioli, Annalisa, Giorgio Rizzoni und Pierluigi Pisu. „Model-Based Fault Detection and Isolation in Automotive Electrical Systems“. In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14504.
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This paper deals with the design and validation, through simulation in a Matlab/Simulink and SABER environment, of model-based diagnostic algorithms for an automotive electric power generation system (EPGS). The EPGS includes alternator with rectifier, a battery, and a voltage regulator. The mathematic models of these subsystems, based on the physics of the processes involved, consist of time-varying nonlinear ODEs. The diagnostic problem focuses on the detection and isolation of a specific set of alternator faults, including belt slipping, rectifier fault and voltage regulator fault. An in-depth analysis of the models is conducted in order to understand the effects of different failure modes on system performance; subsequently, an equivalent input-output model of the alternator is formulated and parameterized. The equivalent model permits considerable simplification of the algorithms. The proposed diagnostic approach is based on the generation of residuals obtained using system models and comparing the predicted and measured value of selected variables, including alternator output current, field voltage, battery current, battery voltage and battery temperature. This paper presents the models, diagnostic algorithms and simulation results.