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Utility of Projection Network for Diagnosis of High Pressure Air Compressor Faults

[+] Author Affiliations
C. Jansuwan, C. James Li

Rensselaer Polytechnic Institute, Troy, NY

Paper No. IMECE2003-43505, pp. 155-159; 5 pages
doi:10.1115/IMECE2003-43505
From:
  • ASME 2003 International Mechanical Engineering Congress and Exposition
  • Manufacturing
  • Washington, DC, USA, November 15–21, 2003
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 0-7918-3720-3 | eISBN: 0-7918-4663-6, 0-7918-4664-4, 0-7918-4665-2
  • Copyright © 2003 by ASME

abstract

The utility of a dynamic neural network, i.e., projection network, was established to diagnose the condition of a 4-stage high pressure air compressor. Network structure and parameter initialization and training methods were developed. Using measurements of the compressor’s four stages’ discharge temperatures and pressures collected under different baseline conditons, 3rd stage suction and exhaust value faults, and an unanticipated 3rd stage cylinder pittings as training data, a 99+% of correct classification rate was accomplished with testing data.

Copyright © 2003 by ASME

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