大坝监测数据多维度LSTM异常检测与恢复

Dam monitoring data multi-dimensional LSTM anomaly detection and recovery

大坝安全监测是大坝安全的重要保障,对监测数据进行异常检测与恢复可有效避免对大坝状态的错误估计和判断,具有重要现实意义。近年来基于深度学习方法的大坝监测数据异常检测受到广泛研究,但现存方法存在数据利用不足、信息挖掘不充分等问题。因此,本文提出一种多维度LSTM异常检测与恢复方法,该方法用LSTM输入多个测点的大坝监测数据对单测点数据进行预测,有效利用了不同测点间的相关信息;最后利用拉依达准则对目标测点进行异常检测。本文利用大渡河瀑布沟水电站真空激光准直监测数据进行案例验证,通过与单维度的LSTM异常检测与恢复方法相比较,验证了所提方法能有效地检测数据异常和预测恢复正常数据,是一种有效的大坝监测数据异常检测与恢复方法。

Dam monitoring data is an important guarantee of dam safety.Anomaly detection and recovery of dam monitoring data can effectively avoid the wrong estimation and judgment of dam status,which has important practical significance.In recent years,there are extensive studies on anomaly detection of dam monitoring data based on deep learning methods.However,the existing methods have some drawbacks such as insufficient data utilization and insufficient information mining.Therefore,a multi-dimensional LSTM anomaly detection and recovery method is proposed in this paper.The dam monitoring data of multiple monitoring points are fed into LSTM to predict the data of single monitoring point,and the relevant information between different monitoring points is effectively utilized.Finally,anomaly detection is performed on the data of the target detection point using Pauta criterion.In this paper,the laser collimation monitoring data of Fudougou Hydropower Station in Dadu River are used for case verification.By comparing with the single dimension LSTM anomaly detection and recovery algorithm,it is verified that the performance of proposed method is effective both in anomaly detection and data recovery,which is an effective method for dam monitoring data anomaly detection and recovery.