Helicopter gearbox diagnostics and prognostics using vibration signature analysis

B. Eugene Parker, Todd M. Nigro, Monica P. Carley, Roger L. Barron, David G. Ward, H. Vincent Poor, Denny Rock, Thomas A. Dubois

Research output: Contribution to journalConference articlepeer-review

22 Scopus citations

Abstract

Rotorcraft safety, survivability, and mission effectiveness depend on the structural integrity of dynamic components. The need exists to develop an on-board, continuous vibration diagnostic system to detect and to prognosticate faults in these components prior to failure. This paper overviews a generic fault detection, isolation, and estimation (FDIE) architecture for condition-based machinery maintenance applications. Neural network-based fault pattern recognition is used to analyze normal and defect vibration signatures in helicopter transmissions. Data from nine seeded-fault test-rig experiments, each corresponding to one of six different fault/no-fault conditions, were used to train and evaluate polynomial neural networks at pattern classification tasks. Features were generated using the amplitude spectra of the time-series vibration signatures. The Algorithm for Synthesis of Polynomial Networks for Classification (CLASS),1 a neural network software package that utilizes a constrained, minimum-logistic-loss criterion for multiclass problems, was used to perform the pattern recognition tasks. By employing a multiple-look post-processing strategy, perfect vibration signature classification was achieved.

Original languageEnglish (US)
Pages (from-to)531-542
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume1965
DOIs
StatePublished - Sep 2 1993
EventApplications of Artificial Neural Networks IV 1993 - Orlando, United States
Duration: Apr 11 1993Apr 16 1993

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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