Abstract: Bridge sensor anomaly detection is a method based on sensor technology to monitor the status of bridge structure in real time. Its purpose is to discover the anomalies of the bridge structure in time and recognize them to prevent and avoid accidents. The author proposes an abnormal signal detection and identification method for bridge sensors based on deep learning technology, and by designing an abnormal data detection algorithm for bridge sensors based on the LSTM (Long Short-Term Memoy) network model, it can realize the effective detection of the abnormal data location of the bridge cable sensor, and the precision rate and recall rate of the abnormal data detection can reach 99. 8% and 95. 3%, respectively. By combining the deep learning network and the actual working situation of bridge sensors, we design the abnormal classification algorithm of bridge cable-stayed force sensor based on CNN(Convolution Neural Networks) network model to realize the intelligent identification of 7 types of signals of bridge cable-stayed force sensor data, and the precision rate of identification of multiple abnormal data types and the recall rate can reach more than 90%. Compared with the current bridge sensor anomaly data detection and classification methods, the author's proposed method can realize the accurate detection of bridge sensor anomaly data and intelligent identification of anomaly types, which can provide a guarantee for the accuracy of bridge sensor monitoring data and the effectiveness of later performance index identification. 

Key words: bridge sensors, abnormal data detection, abnormal data classification, deep learning