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

针对基于传感器技术实时监测桥梁结构状态,为及时发现桥梁结构的异常情况并进行判识,预防和避免事故的发生,提出了基于深度学习技术的桥梁传感器异常信号检测和识别方法。通过设计基于LSTM(Long Short-Term Memoy)网络模型的桥梁传感器异常数据检测算法,实现桥梁索力传感器异常数据位置的有效检测,异常数据检测精确率与召回率分别达到99.8%、95.3%。通过将深度学习网络和桥梁传感器实际工作情况相结合,设计基于CNN(Convolutional Neural Networks)网络模型的桥梁索力传感器异常分类算法,实现桥梁索力传感器数据7类信号的智能识别,多种异常数据类型识别精确率与召回率超过90%。相对于目前桥梁传感器异常数据检测和分类方法,该方法能实现桥梁传感器异常数据和类型的精准检测和智能识别,为桥梁传感器监测数据的准确性与后期性态指标识别的有效性提供保障。

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.