Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems

Abhinav Saxena, Ashraf Saad

Research output: Contribution to journalArticlepeer-review

184 Scopus citations

Abstract

We present the results of our investigation into the use of genetic algorithms (GAs) 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 a 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.

Original languageEnglish
Pages (from-to)441-454
Number of pages14
JournalApplied Soft Computing
Volume7
Issue number1
DOIs
StatePublished - Jan 2007

Keywords

  • Artificial neural networks
  • Condition monitoring
  • Fault diagnosis
  • Genetic algorithms
  • Hybrid techniques
  • Rotating mechanical systems

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