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Application of PCA for early leak detection in a pipeline system of a steam boiler

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PL
Zastosowanie metody PCA do wczesnego wykrywania wycieków w rurociągach kotła parowego
Języki publikacji
PL
Abstrakty
PL
W artykule przedstawiono zastosowanie metody składowych głównych (PCA) do wczesnego wykrywania wycieków z rurociągów kotła parowego pracującego w elektrociepłowni miejskiej. Model PCA, zbudowany na podstawie pomiarów 12 wybranych zmiennych procesowych, przedstawiony w przestrzeni trzech składowych głównych (PC) o największych modułach, został wykorzystany do określenia tzw. elipsoidy ufności, tj. obszaru w przestrzeni PC, w którym mieszczą się wartości zmiennych odpowiadające poprawnemu działaniu systemu. Zmiany aktualnego punktu pracy kotła tworzyły tzw. trajektorię uszkodzenia w przestrzeni PC i były podstawą do podejmowania decyzji na temat ew. wycieku z rurociągów.
EN
The application of the Principal Component Analysis (PCA) method for early detection of leakages in the pipeline system of a steam boiler in a thermal-electrical power plant is presented and discussed. The PCA model built from historical measurements of 12 selected process variables, mapped to the reduced space of three Principal Components (PC) of the highest magnitude, was used to establish the confidence ellipsoid, i.e. the feasible region in the PC coordinates, occupied by the values of process variables related to the ‘healthy’ system. Changes of the current location of the process operating point in the PC space created the ‘fault trajectory’ and were the basis for making a decision of leakage detection.
Rocznik
Strony
190--203
Opis fizyczny
Bibliogr. 49 poz., rys., tab.
Twórcy
  • Bialystok University of Technology, Faculty of Electrical Engineering, ul. Wiejska 45D, 15-351 Bialystok, Poland
  • Bialystok University of Technology, Faculty of Electrical Engineering, ul. Wiejska 45D, 15-351 Bialystok, Poland
Bibliografia
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  • [30] Ghaffari A., Chaibakhsh A., Lucas C., Soft computing approach for modeling power plant with a once-through boiler, Engineering Applications of Artificial Intelligence, 20 (2007), No. 6, 809-819
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  • [35] Deng P.C., Gui W.H., Xie, Y.F., Latent space transformation based on principal component analysis for adaptive fault detection, IET Control Theory & Applications, 4 (2010), No. 11, 2527-2538
  • [36] Ma Y.-G., Zhang J., Fault Diagnosis based on PCA and D-S Evidence Theory, Asia-Pacific Power and Energy Engineering Conference, APPEEC 2009, 28-31 March 2009, Wuhan, China, 1-5
  • [37] Lau C.K., Ghosh K., Hussain M.A., Che Hassan C.R., Fault diagnosis of Tennessee Eastman process with multi-scale PCA and ANFIS, Chemometrics and Intelligent Laboratory Systems, 120 (2013), 1-14
  • [38] Ghamari A., Khaloozadeh H., Ashraf-Modarres A., Ghamari H., Application of Quantitative Data-Based Fault Detection Methods on a Drum-Type Boiler, Proceedings of the 3rd Conference on Thermal Power Plants (CTPP), 2011, 1-6
  • [39] Jaffel I., Taouali O., Elaissi I., Messaoud H., Comparative study of PCA approaches for fault detection: Application to a chemical reactor, IEEE - 2013 International Conference on Control, Decision and Information Technologies CoDIT'13, 6- 8 May 2013, Hammamet, Tunisia, 57-62
  • [40] Ding S., Zhang P., Ding E., Yin S., Naik A., Deng P., Gui W., On the Application of PCA Technique to Fault Diagnosis, Tsinghua Science and Technology, 15 (2010), No. 2, 138- 144
  • [41] Tamura M., Tsujita S., A study on the number of principal components and sensitivity of fault detection using PCA, Computers and Chemical Engineering, 31 (2007), No. 9, 1035-1046
  • [42] Huang Y., Gertler J., McAvoy T.J., Sensor and actuator fault isolation by structured partial PCA with nonlinear extensions, Journal of Process Control, 10 (2000), No. 5, 459-469
  • [43] Yoo C.K., Lee J.-M., Vanrolleghem P.A., Lee I.-B., On-line monitoring of batch processes using multiway independent component analysis, Chemometrics and Intelligent Laboratory Systems, 71 (2004), No. 2, 151-163
  • [44] Cheng H., Nikus M., Jämsä-Jounela S.-L., Evaluation of PCA methods with improved fault isolation capabilities on a paper machine simulator, Chemometrics and Intelligent Laboratory Systems, 92 (2008), No. 2, 186-199
  • [45] Yu J., Yoo J., Jang J., Park J.H., Kim S., A novel plugged tube detection and identification approach for final super heater in thermal power plant using principal component analysis. Energy, vol. 26 (2017), 1 May 2017, 404-418
  • [46] Niu Z., Liu J.-Z., Niu Y.-G., Pan Y.-S, A Reformative PCAbased Fault Detection Method Suitable for Power Plant Process, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, 2133-2138
  • [47] Tong P., An L.-S., Zhang J., Liu Y.-T., Research on Fault Diagnosis Method of Principal Components Analysis and D-S Evidence Theory, Chinese Control and Decision Conference, CCDC '09, 2009, 1601-1605
  • [48] Sun X., Marquez H.J., Chen T., Riaz M., An improved PCA method with application to boiler leak detection, ISA Transactions, 44 (2005), 379-397
  • [49] Pawlik M., Strzelczyk F., Elektrownie (Power plants) - in Polish, WNT, Warszawa, Poland, 2009
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-35053e68-0ec5-497c-9645-688bcdc12d89
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