Fault Detection of Helicopter Gearboxes Using the Multi-Valued Influence Matrix Method

Abstract

Fault detection of a helicopter gearbox by pattern classification is discussed. The detection system is composed of two components, a quantization matrix to flag the measurements, and a multi-valued influence matrix (MVIM) that represents the behavior of measurements during normal operation and at fault instances. Both the quantization matrix and the influence matrix are tuned during a training session so as to minimize the error in detection. This detection system was applied to vibration measurements collected from a helicopter gearbox test stand during accelerated fatigue tests and at various fault instances. The results indicate that the MVIM method provides accurate detection when the full range of faults effects on the measurements are included in the training set. Furthermore, the fixed structure of MVIM allows evaluation of individual measurements. This feature was utilized to select a subset of measurements crucial to detection.