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Fault classification using an Artificial Neural Network based on Vibrations from a Reciprocating Compressor

Ahmed, Mahmud and Gu, Fengshou (2010) Fault classification using an Artificial Neural Network based on Vibrations from a Reciprocating Compressor. In: Future Technologies in Computing and Engineering: Proceedings of Computing and Engineering Annual Researchers' Conference 2010: CEARC’10. University of Huddersfield, Huddersfield, pp. 92-97. ISBN 9781862180932

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      Abstract

      Reciprocating compressors are widely used in industry for various purposes and faults occurring in them can degrade performance, consume additional energy, and even cause severe damage to the machine. This paper will develop an automated approach to condition classification of a reciprocating compressor based on vibration measurements. Both the time domain and frequency domain techniques have been applied to the vibration signals and a large number of candidate features have been obtained based on previous studies. A subset selection method has then been used to configure a probabilistic neural network (PNN), with high computational efficiency, for effective fault classifications. The results show that a 95.50% correct classification between four different faulty cases is the best result when using a subset of frequency feature, whereas a 93.05% rate is the best for the subset from the time domain.

      Item Type: Book Chapter
      Uncontrolled Keywords: Condition Monitoring, Probabilistic Neural Network, Reciprocating Compressor, Timedomain and Frequency-domain features.
      Subjects: T Technology > T Technology (General)
      T Technology > TJ Mechanical engineering and machinery
      Schools: School of Computing and Engineering
      School of Computing and Engineering > Automotive Engineering Research Group
      School of Computing and Engineering > Computing and Engineering Annual Researchers' Conference (CEARC)
      School of Computing and Engineering > Diagnostic Engineering Research Centre
      School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
      School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
      School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
      Related URLs:
      Depositing User: Sharon Beastall
      Date Deposited: 13 Jan 2011 11:50
      Last Modified: 07 Oct 2011 16:12
      URI: http://eprints.hud.ac.uk/id/eprint/9318

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