Genetic Algorithms for Artificial Neural Net-based Condition Monitoring System Design for Rotating Mechanical Systems

  • Abhinav Saxena
  • Ashraf Saad
Conference paper

DOI: 10.1007/3-540-31662-0_11

Part of the Advances in Soft Computing book series (AINSC, volume 34)
Cite this paper as:
Saxena A., Saad A. (2006) Genetic Algorithms for Artificial Neural Net-based Condition Monitoring System Design for Rotating Mechanical Systems. In: Abraham A., de Baets B., Köppen M., Nickolay B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg

Abstract

We present the results of our investigation into the use of Genetic Algorithms (GA) for identifying near optimal design parameters of Diagnostic Systems that are based on Artificial Neural Networks (ANNs) for condition monitoring of mechanical systems. ANNs have been widely used for health diagnosis of mechanical bearing using features extracted from vibration and acoustic emission signals. However, different sensors and the corresponding features exhibit varied response to different faults. Moreover, a number of different features can be used as inputs to a classifier ANN. Identification of the most useful features is important for an efficient classification as opposed to using all features from all channels, leading to very high computational cost and is, consequently, not desirable. Furthermore, determining the ANN structure is a fundamental design issue and can be critical for the classification performance. We show that GA can be used to select a smaller subset of features that together form a genetically fit family for successful fault identification and classification tasks. At the same time, an appropriate structure of the ANN, in terms of the number of nodes in the hidden layer, can be determined, resulting in improved performance.

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Copyright information

© Springer 2006

Authors and Affiliations

  • Abhinav Saxena
    • 1
  • Ashraf Saad
    • 2
  1. 1.School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Associate Professor, School of Electrical and Computer EngineeringGeorgia Institute of TechnologySavannahUSA

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