Full Content is available to subscribers

Subscribe/Learn More  >

State Forecasting for Rotary Machine Based on Neural Network and Genetic Algorithm

[+] Author Affiliations
Hongmei Liu, Shaoping Wang, Pingchao Ouyang

Beihang University, Beijing, China

Paper No. IMECE2007-41746, pp. 17-21; 5 pages
  • ASME 2007 International Mechanical Engineering Congress and Exposition
  • Volume 4: Design, Analysis, Control and Diagnosis of Fluid Power Systems
  • Seattle, Washington, USA, November 11–15, 2007
  • Conference Sponsors: ASME
  • ISBN: 0-7918-4298-3 | eISBN: 0-7918-3812-9
  • Copyright © 2007 by ASME


A state forecasting is a key technology to achieve the advanced predictive maintenance. A Prediction based on neural network is a new approach to realize the state predicting. The present neural networks predicting models are comparatively poor in adaptability to environment and in predicting accuracy, therefore, a new rotary machine online state forecasting method based on the genetic algorithm (GA) and neural network (NN) was presented. GA was used for dynamical optimizing the structure parameters of BP network to obtain the optimal network structure. A training algorithm combining GA with BP was adopted to avoid the local minimum and to heighten the learning precision. The state predicting results for hydraulic pump indicate that the predicting model purposed may dynamically optimize the structure parameters in accordance with different conditions, and gained satisfactory results.

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