Pattern Classifier for Health Monitoring of Helicopter Gearboxes
Abstract
The application of a newly developed diagnostic method to a helicopter gearbox is demonstrated. This method is a pattern classifier which uses a multi-valued influence matrix (MVIM) as its diagnostic model. The method benefits from a fast learning algorithm, based on error feedback, that enables it to estimate gearbox health from a small set of measurement-fault data. The MVIM method can also assess the diagnosability of the system and variability of the fault signatures as the basis to improve fault signatures. This method was tested on vibration signals reflecting various faults in an OH-58A main rotor transmission gearbox. The vibration signals were then digitized and processed by a vibration signal analyzer to enhance and extract various features of the vibration data. The parameters obtained from this analyzer were utilized to train and test the performance of the MVIM method in both detection and diagnosis. The results indicate that the MVIM method provided excellent detection results when the full range of faults effects on the measurements were included in training, and it had a correct diagnostic rate of 95% when the faults were included in training.... Detection, Diagnosis, Helicopter gearbox, Pattern classification, Vibration signal processing.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 1993
- Accession Number
- ADA263112
Entities
People
- David G. Lewicki
- Hsinyung Chin
- Kourosh Danai
Organizations
- National Aeronautics and Space Administration