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

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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.

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Authors and Affiliations

  1. Department of Production and Quality Engineering, Norwegian University of Science and Technology, S. P. Andersensveien 5, Valgrinda, 7491, Trondheim, Norway

    Zhenyou Zhang & Kesheng Wang

  2. School of Materials, The University of Manchester, Sackville St Blg, Manchester, M13 9PL, UK

    Yi Wang

Authors
  1. Zhenyou Zhang
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  2. Yi Wang
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  3. Kesheng Wang
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Correspondence to Kesheng Wang.

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Zhang, Z., Wang, Y. & Wang, K. Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. J Intell Manuf 24, 1213–1227 (2013). https://doi.org/10.1007/s10845-012-0657-2

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  • DOI: https://doi.org/10.1007/s10845-012-0657-2

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