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Case study on the effectiveness of conditio monitoring techniques for fault diagnosis of pumps in thermal power plant

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A case study was carried out to investigate the effectiveness of condition monitoring techniques in the early ailure detection of pumps in a thermal power plant. Various condition monitoring techniques used in this case study involved vibration analysis, motor current signature analysis, noise monitoring and wear debris analysis. These techniques were applied on the three pumps, namely boiler feed water pump, auxiliary cooling water pump and condensate extraction pump, which have to work continuously for the operation of the thermal power plant. Vibration analysis of the auxiliary cooling water pump showed that there is a rising trend in the acceleration values at its driving and non-driving end indicating the deterioration of bearings. Motor current index range of all the pumps was found to be within acceptable limits. Wear debris analysis of lubricant in the hydraulic coupling of boiler feed water pump indicated the presence of sand, dirt and low alloy steel sliding wear particles in it. Condition monitoring techniques have been proved to be an effective technique in early failure detection of pumps.
Słowa kluczowe
Rocznik
Strony
70--75
Opis fizyczny
Bibliogr. 18 poz., fot. kolor., rys., wykr.
Twórcy
  • Mechanical Engineering Department, University of Petroleum and Energy Studies, School of Engineering, Dehradun 248007, Uttarakhand, India
  • Mechanical Engineering Department, University of Petroleum and Energy Studies, School of Engineering, Dehradun 248007, Uttarakhand, India
Bibliografia
  • [1] Medica-Viola, V., Pavković, B. and Mrzljak, V.: Numerical model for on-condition monitoring of condenser in coal-fired power plants, Int. J. Heat Mass Transf., 117, Supplement C, 912-923, 2018.
  • [2] West, G. M., McArthur, S. D. J. and Towle, D.: “Industrial implementation of intelligent system techniques for nuclear power plant condition monitoring, Expert Syst. Appl., 39(8), 7432-7440, 2012.
  • [3] Beebe, R. S.: 6 - Vibration analysis of pumps - basic, in: Predictive Maintenance of Pumps Using Condition Monitoring, R. S. Beebe, Ed. Amsterdam: Elsevier Science,. 83-100, 2004.
  • [4] De Michelis, C., Rinaldi, C., Sampietri, C. and Vario, R.: 2 - Condition monitoring and assessment of power plant components, in: Power Plant Life Management and Performance Improvement, Oakey, J.E. , Ed. Woodhead Publishing, 38-109, 2011.
  • [5] Adamkowski, A., Henke, A. and Lewandowski, M.: Resonance of torsional vibrations of centrifugal pump shafts due to cavitation erosion of pump impellers, Eng. Fail. Anal., 70, Supplement C, 56-72, 2016.
  • [6] Siano, D., Frosina, E. and Senatore, A.: Diagnostic Process by Using Vibrational Sensors for Monitoring Cavitation Phenomena in a Getoror Pump Used for Automotive Applications, Energy Procedia, 126, Supplement C, . 1115-1122, 2017.
  • [7] Roque, A., Calado, J. M. F. and Ruiz, J. M.: Vibration Analysis versus Current Signature Analysis, IFAC Proc. Vol., 45(20), 794- 799, 2012.
  • [8] C. Kurien and A. K. Srivastava.: “Condition monitoring of systems in thermal power plant for vibration , motor signature , noise and wear debris analysis, World Sci. News, 91, December 2017, 31-43, 2018.
  • [9] Dlamini, V., Naidoo, R. and Manyage, M.: A non-intrusive method for estimating motor eflciency using vibration signature analysis, Int. J. Electr. Power Energy Syst., 45(1), 384-390, 2013.
  • [10] Pires, V.F., Kadivonga, M., Martins, J. F. and Pires, A.J.: Motor square current signature analysis for induction motor rotor diagnosis, Measurement, 46(2), 942-948, 2013.
  • [11] Bravo-Imaz, I., Ardakani, H. D., Liu, Z., García-Arribas, A., Arnaiz, A. and Lee, J.: Motor current signature analysis for gearbox condition monitoring under transient speeds using wavelet analysis and dual-level time synchronous averaging, Mech. Syst. Signal Process., vol. 94, Supplement C, 73-84, 2017.
  • [12] Maijala, P., Shuyang, Z., Heittola, T. and Virtanen, T.: Environmental noise monitoring using source classification in sensors, Appl. Acoust., 129, Supplement C, 258-267, 2018.
  • [13] Albert, D. G. and Decato, S. N.: Acoustic and seismic ambient noise measurements in urban and rural areas, Appl. Acoust., 119, Supplement C, 135-143, 2017.
  • [14] Raadnui, S. and Kleesuwan, S.: Electrical pitting wear debris analysis of grease-lubricated rolling element bearings, Wear, 271(9), 1707-1718, 2011.
  • [15] Costa, H. L., Junior, M. M. O. and de Mello, J. D. B.: Effect of debris size on the reciprocating sliding wear of aluminium, Wear, 376-377, Part B, 1399-1410, 2017.
  • [16] Peng, Y. et al.: A hybrid search-tree discriminant technique for multivariate wear debris classification, Wear, 392-393, Supplement C, 152-158, 2017.
  • [17] Feng, S., Fan, B., Mao, D. and Xie, Y.: Prediction on wear of a spur gearbox by on-line wear debris concentration monitoring, Wear, 336-337, Supplement C, 1-8, 2015.
  • [18] Kurien, C. and Srivastava, A. K.: Investigation on power aspects in impressed current cathodic protection system, J. Corros. Sci. Eng., 20, 2017.
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-12fdcfbf-1249-41ce-9c15-fe924d2980b9
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