Full Content is available to subscribers

Subscribe/Learn More  >

A Neural Network Model Based Approach to Detect Seal and Impeller Failures in Centrifugal Pumps

[+] Author Affiliations
Balaje T. Thumati, Jeffery Birt, Neha Bassi, Jag Sarangapani

University of Missouri at Rolla, Rolla, MO

Paper No. IMECE2007-41941, pp. 665-672; 8 pages
  • ASME 2007 International Mechanical Engineering Congress and Exposition
  • Volume 10: Mechanics of Solids and Structures, Parts A and B
  • Seattle, Washington, USA, November 11–15, 2007
  • Conference Sponsors: ASME
  • ISBN: 0-7918-4304-1 | eISBN: 0-7918-3812-9
  • Copyright © 2007 by ASME


With the increased complexity of today’s industrial processes, maintaining equipment by preventing unscheduled downtime using monitoring hardware is a key challenge. Industrial statistics indicate that seal and impeller failures are predominant failure modes in centrifugal pumps and they are not adequately addressed in the literature. In this paper, a neural network (NN) based Nonlinear Autoregressive Moving Average with Exogenous input (NARMAX) model is used to develop fault detection scheme for detecting seal and impeller failures in centrifugal pumps. A rigorous methodology of detecting failures at the incipient stage is introduced. First a nonlinear relationship among the monitored parameters (inlet and outlet pressure, outlet flow, inlet and outlet temperature, and acceleration) where the previous values of the indicative parameters are used as inputs to the NARMAX model and the output being the value at the current instance is captured. The NARMAX modeled outputs are compared with the actual measured values in order to generate residuals. By choosing a suitable threshold, we could minimize false and missed alarms. Mathematical procedure for selection of threshold is derived in this paper. Along with the NARMAX model, an online approximator is used in the fault detection scheme for understanding the faults in the system. Experiments on the centrifugal pump seal and impeller failures were conducted by using a laboratory test bed. Experimental results show that the proposed fault detection scheme is able to successfully detect failures.

Copyright © 2007 by ASME



Interactive Graphics


Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In