Wear Life Prediction of Sliding Bearings Based on Multitype Monitoring Data of Bridges

Abstract

Sliding bearings are a key component of bridges, and their normal performance is an important prerequisite to ensure traffic safety. To solve the problem wherein a single type of sensor has difficulty in effectively predicting the wear life of sliding bearings, this paper proposes a method to predict the sliding bearing wear life using the multitype monitoring data of the bridge. This method uses the vertical acceleration data of the girder with high sampling frequency to calculate the bearing cumulative dynamic displacement under vehicle load and then processes the data collected by the longitudinal displacement gauge with low sampling frequency to extract the bearing cumulative static displacement under the effect of temperature. Next, the daily bearing cumulative displacement calculated by adding them is taken as the evaluation index, and the sliding bearing wear life is predicted based on the reliability analysis. Finally, the proposed method is verified by a numerical example of vehicle–bridge interaction and real bridge monitoring data. The obtained results show that the proposed method can be used to estimate the bearing cumulative displacement with high accuracy and can effectively predict the wear life of sliding bearings. The prediction results can provide an important reference for bridge evaluation.