Practical gear crack prognosis via gear condition index fusion, gear dynamic simulator, and fast crack growth model

Abstract

Gears are common mechanical components used in power transmissions and frequently responsible for transmission failures. The aim of this study is to establish a gear crack prognostic methodology to predict the residual life of a cracked spur gear by integrating a fracture mechanics-based failure model, a gear dynamic simulator, and an existing gear crack diagnostic algorithm that employs an artificial neural network to estimate crack size from measured gear vibration by fusing a number of selected gear condition indices. The estimated crack size and predicted gear residual life were validated with experimental data. Experimental results showed that the method had an averaged error of 12.94 per cent in its prediction of residual life.