In order to be able to detect the abnormal condition of sensors in time， a sensor abnormality detection model for bridge health monitoring system based on gray correlation analysis is designed. First， the datas collected from multiple strain sensors in normal operation and single sensor abnormality are analyzed by gray correlation analysis， respectively， and the least correlated count columns that characterize the degree of geometric similarity between each column of sensor data and the rest of the columns of data are obtained. The comparison revealed that the distribution of the least correlated counts when there was no abnormality and when there was an abnormality showed marked differences， thus verifying the feasibility of this method； then， a weight calculation strategy was designed to transform the least correlated count columns into a normalized value and use it as an evaluation index， which quantified the degree of correlation between each column of sensor datas and the rest of the columns of datas； finally， through the analysis of the evaluation indexes of multiple sets of strain datas， the multi-threshold warning mechanism was set up to realize the corresponding determination of different degrees of sensor anomalies. On another set of acceleration monitoring datas to simulate a variety of degrees of anomalies and detection， the results show an overall anomaly recognition rate of over 90%.