吉林大学学报（信息科学版）2024，Vol.42Issue(5)：847-855,9.
基于深度学习的桥梁索力传感器异常数据识别方法
Method for Recognizing Anomalous Data from Bridge Cable Force Sensors Based on Deep Learning
摘要
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.关键词
桥梁传感器/异常数据检测/异常数据分类/深度学习Key words
bridge sensors/abnormal data detection/abnormal data classification/deep learning分类
信息技术与安全科学引用本文复制引用
刘宇,吴红林,闫泽一,文世纪,张连振..基于深度学习的桥梁索力传感器异常数据识别方法[J].吉林大学学报（信息科学版）,2024,42(5):847-855,9.基金项目
国家重点研发计划基金资助项目(2022YFC3801100) （2022YFC3801100）
