Gear and bearing diagnostics using neural network-based amplitude and phase demodulation

摘要

This paper is concerned with diagnostic methods for gears and bearings, the goals of which are to detect incipient structural and metallurgical faults, characterize their nature and severity, and isolate them to particular components. Such information is invaluable for prognostic purposes. The methodology presented herein focuses on the analysis of vibration signals induced by gears and bearings and, specifically, on a means of extracting from such vibration patterns amplitude and phase modulation signals. Such modulation terms, we conjecture, are simple indicators of the severity of a particular type of localized component defect. We show that such amplitude and phase modulation information can be extracted using a neural network computational methodology that relies on nothing more than knowledge of the bearing geometry and the frequency tones at which a given type of defect will manifest itself. We present numerical simulation examples and show that the technique requires extremely few a priori assumptions.