Application of the Laplace-wavelet combined with ANN for rolling bearing fault diagnosis

Khalid F. Al-Raheem, Asok Roy, K. P. Ramachandran, David K. Harrison, Steven Grainger

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)


A new technique for an automated detection and diagnosis of rolling bearing faults is presented. The time-domain vibration signals of rolling bearings with different fault conditions are preprocessed using Laplace-wavelet transform for features' extraction. The extracted features for wavelet transform coefficients in time and frequency domains are applied as input vectors to artificial neural networks (ANNs) for rolling bearing fault classification. The Laplace-Wavelet shape and the ANN classifier parameters are optimized using a genetic algorithm. To reduce the computation cost, decrease the size, and enhance the reliability of the ANN, only the predominant wavelet transform scales are selected for features' extraction.

Original languageEnglish
Pages (from-to)51007
Number of pages1
JournalJournal of Vibration and Acoustics
Issue number5
Publication statusPublished - 1 Oct 2008


  • rolling bearing faults
  • Laplace-wavelet transform
  • mechanical engineering


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