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Journal articles on the topic 'Fault isolation and isolability'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Laursen, Morten, Mogens Blanke, and Dilek Düştegör. "Fault Diagnosis Of A Water For Injection System Using Enhanced Structural Isolation." International Journal of Applied Mathematics and Computer Science 18, no. 4 (2008): 593–604. http://dx.doi.org/10.2478/v10006-008-0052-5.

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Fault Diagnosis Of A Water For Injection System Using Enhanced Structural IsolationA water for injection system supplies chilled sterile water as a solvent for pharmaceutical products. There are ultimate requirements for the quality of the sterile water, and the consequence of a fault in temperature or in flow control within the process may cause a loss of one or more batches of the production. Early diagnosis of faults is hence of considerable interest for this process. This study investigates the properties of multiple matchings with respect to isolability, and it suggests to explore the top

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2

Farina, Marcello, and Andrea Caspani. "Model-based fault isolability and isolation of persistent faults: Centralized and distributed implementations." Systems & Control Letters 156 (October 2021): 105006. http://dx.doi.org/10.1016/j.sysconle.2021.105006.

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3

Hafaifa, Ahmed, Ferhat Laaouad, and Kouider Laroussi. "Fuzzy modeling and control for detection and isolation of surge in industrial centrifugal compressors." Journal of Automatic Control 19, no. 1 (2009): 19–26. http://dx.doi.org/10.2298/jac0901019h.

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This paper provides the possible application of the fuzzy approaches in fault detection and isolation area for a class of complex industrial processes with uncertain interval parameters. The main idea of fuzzy fault detection and isolation is to build a model of a diagnosis procedures, using rules-based Fuzzy Expert System, capable to minimize false alarms enhance detectability and isolability and minimize detection time by hardware implementation to improve reliability, safety and global efficiency. This paper illustrates an alternative implementation to the compression systems supervision ta

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4

Khorasgani, Hamed, Ahmed Farahat, and Chetan Gupta. "Data-driven Residual Generation for Early Fault Detection with Limited Data." Annual Conference of the PHM Society 12, no. 1 (2020): 9. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1162.

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   Traditionally, fault detection and isolation community have used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems. Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation. However, in many complex systems it is not feasible to develop highly accurate models for the systems and to keep the models updated during the system lifetime. Recently, data- driven solutions have received an immense attention in the industrial appl

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5

Kóscielny, Jan Maciej, Michał Syfert, Kornel Rostek, and Anna Sztyber. "Fault isolability with different forms of the faults–symptoms relation." International Journal of Applied Mathematics and Computer Science 26, no. 4 (2016): 815–26. http://dx.doi.org/10.1515/amcs-2016-0058.

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Abstract The definitions and conditions for fault isolability of single faults for various forms of the diagnostic relation are reviewed. Fault isolability and unisolability on the basis of a binary diagnostic matrix are analyzed. Definitions for conditional and unconditional isolability and unisolability on the basis of a fault information system (FIS), symptom sequences and directional residuals are formulated. General definitions for conditional and unconditional isolability and unisolability in the cases of simultaneous evaluation of diagnostic signal values and a sequence of symptoms are

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6

Basseville, Michéle. "On Fault Detectability and Isolability." European Journal of Control 7, no. 6 (2001): 625–37. http://dx.doi.org/10.3166/ejc.7.625-637.

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7

Sharifi, Reza, and Reza Langari. "Isolability of faults in sensor fault diagnosis." Mechanical Systems and Signal Processing 25, no. 7 (2011): 2733–44. http://dx.doi.org/10.1016/j.ymssp.2011.02.015.

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8

Jung, Daniel, Erik Frisk, and Mattias Krysander. "Quantitative isolability analysis of different fault modes." IFAC-PapersOnLine 48, no. 21 (2015): 1275–82. http://dx.doi.org/10.1016/j.ifacol.2015.09.701.

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9

Shumsky, A., and M. Staroswiecki. "Fault Detectability and Isolability for Nonlinear Uncertain Systems." IFAC Proceedings Volumes 36, no. 5 (2003): 603–8. http://dx.doi.org/10.1016/s1474-6670(17)36558-8.

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10

Chi, Guoyi, and Danwei Wang. "Sensor Placement for Fault Isolability Based on Bond Graphs." IEEE Transactions on Automatic Control 60, no. 11 (2015): 3041–46. http://dx.doi.org/10.1109/tac.2015.2409952.

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11

Düştegör, Dilek, Erik Frisk, Vincent Cocquempot, Mattias Krysander, and Marcel Staroswiecki. "Structural analysis of fault isolability in the DAMADICS benchmark." Control Engineering Practice 14, no. 6 (2006): 597–608. http://dx.doi.org/10.1016/j.conengprac.2005.04.008.

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12

Chen, Qi, Jincheng Wang, and Qadeer Ahmed. "Design and Evaluation of a Structural Analysis-Based Fault Detection and Identification Scheme for a Hydraulic Torque Converter." Sensors 18, no. 12 (2018): 4103. http://dx.doi.org/10.3390/s18124103.

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A hydraulic torque converter (HTC) is a key component in an automatic transmission. To monitor its operating status and to detect and locate faults, and considering the high-efficiency fault detection and identification (FDI) scheme design by the methodology of structural analysis (SA), this paper presents an SA-based FDI system design and validation for the HTC. By the technique of fault mode and effect analysis (FMEA), eight critical faults are obtained, and then two fault variables are chosen to delegate them. Fault detectability and isolability, coupled with different sensor placements, ar

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13

Trothe, Max Emil S., Hamid Reza Shaker, Muhyiddine Jradi, and Krzysztof Arendt. "Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings." Energies 12, no. 9 (2019): 1601. http://dx.doi.org/10.3390/en12091601.

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Faults and anomalies in buildings are among the main causes of building energy waste and occupant discomfort. An effective automatic fault detection and diagnosis (FDD) process in buildings can therefore save a significant amount of energy and improve the comfort level. Fault diagnosability analysis and an optimal FDD-oriented sensor placement are prerequisites for effective, efficient and successful diagnostics. This paper addresses the problem of fault diagnosability for smart buildings. The method used in the paper is a model-based technique which uses Dulmage-Mendelsohn decomposition. To t

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14

Park, S., Y. Park, and Y. S. Park. "Degree of fault isolability and active fault diagnosis for redundantly actuated vehicle system." International Journal of Automotive Technology 17, no. 6 (2016): 1045–53. http://dx.doi.org/10.1007/s12239-016-0102-1.

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15

Kinnaert, M., M. Nyberg, and M. Basseville. "Discussion on:‘On Fault Detectability and Isolability’ by M. Basseville." European Journal of Control 7, no. 6 (2001): 638–41. http://dx.doi.org/10.3166/ejc.7.638-641.

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16

Mnassri, Baligh, El Mostafa El Adel, and Mustapha Ouladsine. "Unified Sufficient Conditions for PCA-based Fault Detectability and Isolability*." IFAC Proceedings Volumes 45, no. 20 (2012): 421–26. http://dx.doi.org/10.3182/20120829-3-mx-2028.00101.

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17

Syfert, Michał. "THE ISSUE OF DIAGNOSTIC RELATION UNCERTAINTY AND FAULT CONDITIONAL ISOLABILITY." IFAC Proceedings Volumes 39, no. 13 (2006): 747–52. http://dx.doi.org/10.3182/20060829-4-cn-2909.00124.

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18

Chi, Guoyi, Danwei Wang, Tung Le, Ming Yu, and Ming Luo. "Sensor Placement for Fault Isolability Using Low Complexity Dynamic Programming." IEEE Transactions on Automation Science and Engineering 12, no. 3 (2015): 1080–91. http://dx.doi.org/10.1109/tase.2014.2372792.

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19

Chen, Qi, Wenfeng Tian, Wuwei Chen, Qadeer Ahmed, and Yanming Wu. "Model-Based Fault Diagnosis of an Anti-Lock Braking System via Structural Analysis." Sensors 18, no. 12 (2018): 4468. http://dx.doi.org/10.3390/s18124468.

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The anti-lock braking system (ABS) is an essential part in ensuring safe driving in vehicles. The Security of onboard safety systems is very important. In order to monitor the functions of ABS and avoid any malfunction, a model-based methodology with respect to structural analysis is employed in this paper to achieve an efficient fault detection and identification (FDI) system design. The analysis involves five essential steps of SA applied to ABS, which includes critical faults analysis, fault modelling, fault detectability analysis and fault isolability analysis, Minimal Structural Over-dete

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20

Chen, Jie, and Ron J. Patton. "A Re-Examination of Fault Detectability and Isolability in Linear Dynamic Systems." IFAC Proceedings Volumes 27, no. 5 (1994): 567–73. http://dx.doi.org/10.1016/s1474-6670(17)48087-6.

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21

Chen, Changxiong, Liping Chen, Jianwan Ding, and Yizhong Wu. "The effectivity analysis of adding sensors for improving model based fault isolability properties." Journal of Process Control 70 (October 2018): 123–32. http://dx.doi.org/10.1016/j.jprocont.2018.07.007.

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22

Mnassri, Baligh, El Mostafa El Adel, and Mustapha Ouladsine. "Generalization and analysis of sufficient conditions for PCA-based fault detectability and isolability." Annual Reviews in Control 37, no. 1 (2013): 154–62. http://dx.doi.org/10.1016/j.arcontrol.2013.04.005.

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23

Polverino, Pierpaolo, Marco Sorrentino, and Cesare Pianese. "Improved Fault Isolability for Solid Oxide Fuel Cell Diagnosis Through Sub-system Analysis." Energy Procedia 105 (May 2017): 1918–23. http://dx.doi.org/10.1016/j.egypro.2017.03.560.

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24

Pourasghar, Masoud, Vicenҫ Puig, and Carlos Ocampo-Martinez. "Interval observer fault detection ensuring detectability and isolability by using a set-invariance approach." IFAC-PapersOnLine 51, no. 24 (2018): 1111–18. http://dx.doi.org/10.1016/j.ifacol.2018.09.727.

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25

Clerici, M., C. Balotta, A. Salvaggio, et al. "Human immunodeficiency virus (HIV) phenotype and interleukin-2/ interleukin-10 ratio are associated markers of protection and progression in HIV infection." Blood 88, no. 2 (1996): 574–79. http://dx.doi.org/10.1182/blood.v88.2.574.bloodjournal882574.

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Human immunodeficiency virus (HIV) isolability, rate of viral replication, HIV phenotype, type 1 and type 2 cytokine production, and CD4 counts were cross sectionally analyzed in 63 HIV seropositive (HIV+) individuals to establish possible correlations between virologic and immunologic markers of protection and progression. We observed that these markers are tightly correlated. Thus, lack or low prevalence of HIV isolability and the presence of nonsyncitium inducing strains are associated with the strongest type 1 cytokine production, the weakest type 2 cytokine production, and highest CD4 cou

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26

Reddy, A., D. Estrin, and R. Govindan. "Large-scale fault isolation." IEEE Journal on Selected Areas in Communications 18, no. 5 (2000): 733–43. http://dx.doi.org/10.1109/49.842989.

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27

Frisk, Erik, Dilek Düştegör, Mattias Krysander, and Vincent Cocquempot. "Improving Fault Isolability Properties by Structural Analysis of Faulty Behavior Models: Application to the DAMADICS Benchmark Problem." IFAC Proceedings Volumes 36, no. 5 (2003): 1107–12. http://dx.doi.org/10.1016/s1474-6670(17)36641-7.

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28

Ryan, P. G., and W. Kent Fuchs. "Dynamic fault dictionaries and two-stage fault isolation." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 6, no. 1 (1998): 176–80. http://dx.doi.org/10.1109/92.661261.

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29

Weber, P., S. Gentil, P. Ripoll, and L. Foulloy. "Multiple Fault Detection And Isolation." IFAC Proceedings Volumes 32, no. 2 (1999): 7903–8. http://dx.doi.org/10.1016/s1474-6670(17)57348-6.

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30

Gómez, Guillermo, and Amaury Lendasse. "Statistical Fault Isolation with PCA." IFAC Proceedings Volumes 33, no. 11 (2000): 1029–34. http://dx.doi.org/10.1016/s1474-6670(17)37497-9.

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31

HOU, M., and P. C. MÜLLER. "Fault detection and isolation observers." International Journal of Control 60, no. 5 (1994): 827–46. http://dx.doi.org/10.1080/00207179408921497.

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32

Reddy, Anoop, Ramesh Govindan, and Deborah Estrin. "Fault isolation in multicast trees." ACM SIGCOMM Computer Communication Review 30, no. 4 (2000): 29–40. http://dx.doi.org/10.1145/347057.347395.

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33

Pomeranz, I., and Y. Zorian. "Fault isolation for nonisolated blocks." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 12, no. 12 (2004): 1412–15. http://dx.doi.org/10.1109/tvlsi.2004.837994.

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34

Wahbe, Robert, Steven Lucco, Thomas E. Anderson, and Susan L. Graham. "Efficient software-based fault isolation." ACM SIGOPS Operating Systems Review 27, no. 5 (1993): 203–16. http://dx.doi.org/10.1145/173668.168635.

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35

Steyer, J. P., L. Lardon, and O. Bernard. "Sensors network diagnosis in anaerobic digestion processes using evidence theory." Water Science and Technology 50, no. 11 (2004): 21–29. http://dx.doi.org/10.2166/wst.2004.0667.

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Instrumentation defines a sensors network on a process. Hardware sensors indeed allow one to get different information sources that can be often cross-checked to provide reliable data. However, each of these sources of information contains some uncertainties, either due to the hardware sensors' measurement principles, to their possible fouling, to the estimated parameters of the models used in software sensors and/or to the specific structures of the software sensors. This paper demonstrates that, in this context, the evidence theory is a very well suited formalism for fault detection and diag

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36

Lu, Lyan-Ywan, Ging-Long Lin, and Tzu-Ching Kuo. "Stiffness controllable isolation system for near-fault seismic isolation." Engineering Structures 30, no. 3 (2008): 747–65. http://dx.doi.org/10.1016/j.engstruct.2007.05.022.

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37

Sohlberg, B., Y. Aloto, A. Erdogan, and E. Erdogan. "MODEL BASED FAULT DETECTION AND ISOLATION BY FAULT PARAMETER ELIMINATION." IFAC Proceedings Volumes 43, no. 9 (2010): 7–12. http://dx.doi.org/10.3182/20100802-3-za-2014.00002.

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38

Guo, Dexing, and Xiaoming Du. "A Fault Isolation Method Based on S1000D Fault Data Module." IOP Conference Series: Materials Science and Engineering 563 (August 9, 2019): 042002. http://dx.doi.org/10.1088/1757-899x/563/4/042002.

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39

Lala, Jaynarayan H. "Fault detection, isolation, and reconfiguration in the fault tolerant multiprocessor." Journal of Guidance, Control, and Dynamics 9, no. 5 (1986): 585–92. http://dx.doi.org/10.2514/3.20150.

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40

Lajic, Zoran, Mogens Blanke, and Ulrik Dam Nielsen. "Fault Isolation for Shipboard Decision Support." IFAC Proceedings Volumes 43, no. 16 (2010): 413–18. http://dx.doi.org/10.3182/20100906-3-it-2019.00072.

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41

Philips, P., K. B. Ramkumar, K. W. Lim, H. A. Preisig, and M. Weiss. "Automaton-based fault detection and isolation." Computers & Chemical Engineering 23 (June 1999): S215—S218. http://dx.doi.org/10.1016/s0098-1354(99)80053-2.

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42

Giovanini, Leonardo, and Arkadiusz Dutka. "Fault Isolation Filter for Nonlinear Systems." IFAC Proceedings Volumes 36, no. 5 (2003): 741–46. http://dx.doi.org/10.1016/s1474-6670(17)36581-3.

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43

Polycarpou, M. M., T. Parisini, and X. Zhang. "Fault Isolation of Nonlinear Uncertain Systems." IFAC Proceedings Volumes 33, no. 11 (2000): 723–28. http://dx.doi.org/10.1016/s1474-6670(17)37446-3.

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44

SHEN, LI-CHENG, and PAU-LO HSU. "Robust Design of Fault Isolation Observers." Automatica 34, no. 11 (1998): 1421–29. http://dx.doi.org/10.1016/s0005-1098(98)00087-9.

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45

Niemann, Henrik, and Niels Kjølstad Poulsen. "Active Fault Isolation in MIMO Systems." IFAC Proceedings Volumes 47, no. 3 (2014): 8012–17. http://dx.doi.org/10.3182/20140824-6-za-1003.01516.

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46

Linker, R., and I. Seginer. "FAULT DETECTION AND ISOLATION IN GREENHOUSES." Acta Horticulturae, no. 534 (August 2000): 367–74. http://dx.doi.org/10.17660/actahortic.2000.534.43.

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47

Jangid, R. S., and J. M. Kelly. "Base isolation for near-fault motions." Earthquake Engineering & Structural Dynamics 30, no. 5 (2001): 691–707. http://dx.doi.org/10.1002/eqe.31.

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48

Gavin, Henri, Cenk Alhan, and Natasha Oka. "Fault Tolerance of Semiactive Seismic Isolation." Journal of Structural Engineering 129, no. 7 (2003): 922–32. http://dx.doi.org/10.1061/(asce)0733-9445(2003)129:7(922).

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49

Beachkofski, Brian, Nate Terning, and John Dlugopolsky. "Fault isolation through test data analysis." Engineering Failure Analysis 16, no. 3 (2009): 987–99. http://dx.doi.org/10.1016/j.engfailanal.2008.08.025.

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50

Kobi, A., S. Nowakowski, and J. Ragot. "Fault detection-isolation and control reconfiguration." Mathematics and Computers in Simulation 37, no. 2-3 (1994): 111–17. http://dx.doi.org/10.1016/0378-4754(94)90012-4.

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