A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network

Jian Da Wu, Yu Hsuan Wang, Peng Hsin Chiang, Mingsian R. Bai

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


An expert system for scooter fault diagnosis using sound emission signals based on adaptive order tracking and neural networks is presented in this paper. The order tracking technique is one of the important approaches for fault diagnosis in rotating machinery. The different faults present different order figures and they can be used to determine the fault in mechanical systems. However, many breakdowns are hard to classify correctly by human experience in fault diagnosis. In the present study, the order tracking problem is treated as a parametric identification and the artificial neural network technique for classifying faults. First, the adaptive order tracking extract the order features as input for neural network in the proposed system. The neural networks are used to develop the training module and testing module. The artificial neural network techniques using a back-propagation network and a radial basis function network are proposed to develop the artificial neural network for fault diagnosis system. The performance of two techniques are evaluated and compared through experimental investigation. The experimental results indicated that the proposed system is effective for fault diagnosis under various engine conditions.

Original languageEnglish
Pages (from-to)49-56
Number of pages8
JournalExpert Systems with Applications
Issue number1
Publication statusPublished - 2009 Jan 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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