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Journal of Intelligent Manufacturing

, Volume 24, Issue 6, pp 1213–1227 | Cite as

Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network

  • Zhenyou Zhang
  • Yi Wang
  • Kesheng WangEmail author
Article

Abstract

This paper proposes a method for classification of fault and prediction of degradation of components and machines in manufacturing system. The analysis is focused on the vibration signals collected from the sensors mounted on the machines for critical components monitoring. The pre-processed signals were decomposed into several signals containing one approximation and some details using Wavelet Packet Decomposition and, then these signals are transformed to frequency domain using Fast Fourier Transform. The features extracted from frequency domain could be used to train Artificial Neural Network (ANN). Trained ANN could predict the degradation (Remaining Useful Life) and identify the fault of the components and machines. A case study is used to illustrate the proposed method and the result indicates its higher efficiency and effectiveness comparing to traditional methods.

Keywords

Diagnosis Prognosis Wavelet packet decomposition Fourier transform and artificial neural network 

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Production and Quality EngineeringNorwegian University of Science and TechnologyTrondheimNorway
  2. 2.School of MaterialsThe University of ManchesterManchesterUK

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