Helicopter gearbox diagnostics and prognostics using vibration signature analysis

B. Eugene Parker Jr.; Todd M. Nigro; Monica P. Carley; Roger L. Barron; David G. Ward; H. Vincent Poor; Dennis Rock; Thomas A. DuBois

doi:10.1117/12.152553

2 September 1993 Helicopter gearbox diagnostics and prognostics using vibration signature analysis

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

Author Affiliations +

Proceedings Volume 1965, Applications of Artificial Neural Networks IV; (1993) https://doi.org/10.1117/12.152553
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

Rotocraft 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), a neural network software package that utilizes a constrained, minimum-logistic-loss criterion for multiclass problem, was used to perform the pattern recognition tasks. By employing a multiple-look post-processing strategy, perfect vibration signature classification was achieved.

Citation Download Citation

B. Eugene Parker Jr., Todd M. Nigro, Monica P. Carley, Roger L. Barron, David G. Ward, H. Vincent Poor, Dennis Rock, and Thomas A. DuBois "Helicopter gearbox diagnostics and prognostics using vibration signature analysis", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); https://doi.org/10.1117/12.152553

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available