Cavitation detection in centrifugal pumps using pressure time-domain features

Cavitation detection in centrifugal pumps using pressure time-domain features

Condition monitoring of centrifugal pumps is vital due to their crucial role in industries. One of the most prevalent faults in pumps is cavitation, which can cause mechanical faults or even failure in the pump. In this paper, an approach is suggested to detect cavitation in a centrifugal pump using time-domain analysis of the pressure signal residual. First, pressure and torque signals are obtained using a model of the electro-pump, and then pressure deviation from the pump performance curve is de ned as a residual. The residual time-domain features are extracted and applied as inputs to a self-organizing map (SOM) neural network to classify the system modes. The results indicate that the suggested method is capable of detecting incipient cavitation. Furthermore, the suggested method demonstrates robust performance against disturbance.

___

  • [1] McKee KK, Forbes G, Mazhar I, Entwistle R, Howard I. A review of major centrifugal pump failure modes with application to the water supply and sewerage industries. In: ICOMS Asset Management Conference; 2011. pp. 32.
  • [2] Al-Hashmi S, Gu F, Li Y, Ball AD, Fen T, Lui K. Cavitation detection of a centrifugal pump using instantaneous angular speed. In: ASME 7th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers; 2004. pp. 185-190.
  • [3] Dalton T, Patton R. Model-based fault diagnosis of a two-pump system. T I Meas Control 1998; 20: 115-124.
  • [4] McKee KK, Forbes GL, Mazhar I, Entwistle R, Howard I. A review of machinery diagnostics and prognostics implemented on a centrifugal pump. In: Engineering Asset Management 2011. Springer; 2014. pp. 593-614.
  • [5] Parrondo JL, Velarde S, Santolaria C. Development of a predictive maintenance system for a centrifugal pump. JQME 1998; 4: 198-211.
  • [6] Jensen J. Detecting Cavitation in Centrifugal Pumps Experimental Results of the Pump Laboratory. ORBIT second quarter 2000; 26-30.
  • [7] Peck JP, Burrows J. On-line condition monitoring of rotating equipment using neural networks. ISA T 1994; 33: 159-164.
  • [8] Wang H, Chen P. Intelligent diagnosis method for a centrifugal pump using features of vibration signals. Neural Comput Appl 2009; 18: 397-405.
  • [9] Sakthivel NR, Sugumaran V, Babudevasenapati S. Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Syst Appl 2010; 37: 4040-4049.
  • [10] Muralidharan V, Sugumaran V. Rough set based rule learning and fuzzy classi cation of wavelet features for fault diagnosis of monoblock centrifugal pump. Measurement 2013; 46: 3057-3063.
  • [11] Sakthivel NR, Nair BB, Elangovan M, Sugumaran V, Saravanmurugan S. Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals. Engineering Science and Technology, an International Journal 2014; 17: 30-38.
  • [12] McKee KK, Forbes G, Mazhar I, Entwistle R, Hodkiewicz M, Howard I. A single cavitation indicator based on statistical parameters for a centrifugal pump. In: Proceedings of the 7th World Congress on Engineering Asset Management (WCEAM 2012). Springer; 2015. pp. 473-481.
  • [13] Alfayez L, Mba D, Dyson G. The application of acoustic emission for detecting incipient cavitation and the best efficiency point of a 60kW centrifugal pump: case study. NDT E Int 2005; 38: 354-358.
  • [14] Farokhzad S, Ahmadi H. Acoustic Based Cavitation Detection of Centrifugal Pump by Neural Network. Journal of Mechanical Engineering and Technology 2013; DOI: 10.18005/JMET0101001.
  • [15] Durocher DB, Feldmeier GR. Predictive versus preventive maintenance. IEEE Ind Appl Mag 2004; 10: 12-21.
  • [16] Hernandez-Solis A, Carlsson F. Diagnosis of submersible centrifugal pumps: a motor current and power signature approaches. EPE J 2010; 20: 58-64.
  • [17] Stopa MM, Filho BJC, Martinez CB. Incipient detection of cavitation phenomenon in centrifugal pumps. IEEE T Ind Appl 2014; 50: 120-126.
  • [18] Kallesoe CS. Fault detection and isolation in centrifugal pumps. PhD, Alborg University, Alborg, Denmark, 2005.
  • [19] Krause PC, Wasynczuk O, Sudhoff SD, Pekarek S. Analysis of Electric Machinery and Drive Systems. New York, NY, USA: John Wiley & Sons, 2013.
  • [20] Kallese CS, Cocquempot V, Izadi-Zamanabadi R. Model based fault detection in a centrifugal pump application. IEEE T Contr Syst T 2006; 14: 204-215.
  • [21] Isermann R. Fault-diagnosis applications: model-based condition monitoring: actuators, drives, machinery, plants, sensors, and fault-tolerant systems. Berlin, Germany: Springer Science & Business Media, 2011.
  • [22] Kubota H, Matsuse K, Nakano T. DSP-based speed adaptive ux observer of induction motor. IEEE T Ind Appl 1993; 2: 344-348.
  • [23] Kohonen T. The self-organizing map. In: Proc IEEE 78; September 1990. pp. 1464-1480.
  • [24] Mingoti SA, Lima JO. Comparing SOM neural network with fuzzy c-means, K-means and traditional hierarchical clustering algorithms. Eur J Oper Res 2006; 174: 1742-1759.
  • [25] Haykin S. Network Networks. A Comprehensive Foundation. Upper Saddle River, NJ, USA: Prentice Hall, 2004.
  • [26] Samanipour P, Poshtan J. Electro Pump Modeling Using Laboratory System Data. In: Power Electronics and Drive Systems Technologies Conference (PEDSTC). IEEE; 2016. pp. 111-115.
Turkish Journal of Electrical Engineering and Computer Sciences-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK
Sayıdaki Diğer Makaleler

Creation of a dynamic model of the electri cation and traction power system of a 25 kV AC feed railway line together with analysis of different operation scenarios using MATLAB/Simulink

Hasan TİRYAKİ, Emrah BAL, Ilhan KOCAARSLAN, Mehmet Taciddin AKÇAY, Sırrı Erdem ULUSOY

Prognosis of muscular dystrophy with extrinsic and intrinsic descriptors through ensemble learning

Sathyavikasini KALIMUTHU, Vijaya VIJAYAKUMAR

High inductance fractal inductors for wireless applications

Akhendra Kumar PADAVALA, Bheema Rao NISTALA

A reduced-order observer based on stator ux estimation with straightforward parameter identi cation for sensorless control of DFIGs

Mohammad Reza AZIZIAN, Rahim AJABI-FARSHBAF, Vahid ESLAMPANAH

Feedback delay compensation of a visual servoing system using a piecewise continuous and current estimator-based observer

Abdeldafia MOHAMMED, Haoping WANG, Yang TIAN

Power oscillation damping control by PSS and DFIG wind turbine under multiple operating conditions

Korakot THANPISIT, Issarachai NGAMROO

Multiverse optimized fuzzy-PID controller with a derivative lter for load frequency control of multisource hydrothermal power system

Amit KUMAR, Sathans SUHAG

Active-only variable-gain low-pass lter for dual-mode multiphase sinusoidal oscillator application

Narongsak MANOSITTHICHAI, Fabian KHATE, Pipat PROMMEE

A meander coupled line wideband power divider with open stubs and DGS for mobile application

Sivaprakash SOMALINGA CHANDRASEKARAN, Sivanantha Raja AVANINATHAN, Pavithra MURUGESAN

EKF-based self-regulation of an adaptive nonlinear PI speed controller for a DC motor

Urwa OMER, Omer SALEEM