Measurement of bearing reaction force by distributed strain variation

Abstract

Bearing is an important transmission component of aero-engine under high-speed and heavy-load conditions. To design a roller bearing with high loading capacity and reliability, it is essential to pay attention to the relationship between external load state (reaction force) and internal load distribution (load distribution). Therefore, a measurement method for bearing reaction force is proposed in this study via load distribution and radial basis function neural network. Different from conventional static reaction force measurement methods, both of direction and magnitude of the reaction force were considered in the proposed method without modifications to bearing. Firstly, an experimental system was designed to investigate the load distribution in a roller bearing under different reaction forces using strain variation measurement. Then, finite element analysis was conducted and simulation results of the strain variations at three interested points match well with the experimental measurements. Finally, radial basis function neural network with strong nonlinear fitting ability is applied to construct the mapping relationship between strain variation and reaction force. The results show that the proposed method can predict the reaction force with high accuracy based on the strain variation at three measuring points.