Full Content is available to subscribers

Subscribe/Learn More  >

Improved Accuracy of Fault Diagnosis of Rotating Machinery Using Wavelet De-Noising and Feature Selection

[+] Author Affiliations
Naresh Juluri

TATA Consultancy Services, Bangalore, KA, India

S. Swarnamani

Indian Institute of Technology – Madras, Chennai, TN, India

Paper No. GT2003-38755, pp. 563-571; 9 pages
  • ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference
  • Volume 1: Turbo Expo 2003
  • Atlanta, Georgia, USA, June 16–19, 2003
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-3684-3 | eISBN: 0-7918-3671-1
  • Copyright © 2003 by ASME


Past literature shows that artificial neural networks (ANN) can be successfully applied for fault diagnosis of rotating machinery but the results reported in the past literature are not satisfying. It is discovered that the main reasons for this poor accuracy are, noise present in the signals and usage of feature set that do not describe the signals accurately. Data obtained in field contains good degree of noise and this noise hurts the performance of the network. Although neural network, as a function estimator removes noise from time series to a certain extent, denoising prior to the modeling can greatly improve its ability to capture valuable information. The features used to describe the vibration signals implicitly define a pattern language. If the language is not expressive enough, it would fail to capture the information that is necessary for classification and hence regardless of the learning algorithm used, the accuracy of the classification function learned would be limited by this lack of information. Signal de-noising and feature selection are therefore highly desirable to improve the classification performance of the network. In this paper de-noising based on wavelet transforms and feature selection process based on genetic algorithms is presented. The implicit assumption made in all the past literature is, multi defects do not occur. But in reality we can find lot of cases with multi defects. So, cases with multi defects are also considered in this paper. GA is also used to select optimum network parameters. A multi layer feed forward neural network (MLFNN) is trained with error back propagation (EBP) learning algorithm. To have a complete understanding of the concepts behind the classification accuracy first the study is started with ideal signals derived by synthesizing different sinusoids composed of possible frequencies and amplitudes of sinusoids normally observed in machine vibrations and faults such as unbalance, misalignment and defects in anti friction bearings. The test results show that the proposed method improves the performance of diagnosis to 99.2% even with 15% random noise in the input signals.

Copyright © 2003 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