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基于TCN-自适应的地下洞室围岩变形异常数据识别OA北大核心CSTPCD

Abnormal data recognition for surrounding rock deformation of underground caverns based on TCN and criterion adaptation

中文摘要英文摘要

水电站地下洞室围岩变形数据具有变化不确定、序列样本短等特点,传统的异常识别方法漏识率、误判率较高.随着智能技术的发展,通过神经网络建立更加可靠的异常识别方法是目前研究的热点,而传统的神经网络存在时序关联性不强和计算模型庞杂等问题.为此,提出了基于时域卷积神经网络(TCN)及标准自适应的地下洞室异常数据识别算法,该算法利用TCN技术,考虑序列的前后关系,建立了更为可靠的序列模型;同时针对地下洞室监测数据特征,通过考虑误差中位数、数据波动和仪器精度3 个方面,突现自适应匹配最优识别准则.将该算法应用在叶巴滩水电站地下洞室围岩变形的异常数据识别中,证明了其可有效避免梯度爆炸、消失,模型耗时较长等问题,极大地提高了异常值分析效率和识别率.相关经验可供类似工程异常监测数据识别时借鉴.

The deformation data of surrounding rock in underground caverns of hydropower stations have the characteristics of uncertain changes and short sequence samples,the traditional abnormal data recognition method has high missed recognition rate and misjudgment rate.With the development of intelligent technology,it is a hot topic to establish a more reliable abnormal data recognition method through neural network.However,the traditional neural network has some problems,such as weak temporal correlation and complex calculation.Therefore,an abnormal data recognition algorithm for surrounding rock deformation of under-ground caverns based on time-domain convolutional neural network(TCN)and criterion adaptation was proposed in this paper.The algorithm considered the relationship between the front and back of the monitoring data sequence,and used TCN technology to establish a more reliable sequence model.At the same time,according to the characteristics of monitoring data of underground cav-erns,the optimal recognition criterion of adaptive matching was realized by considering three aspects of error median,data fluctua-tion and instrument accuracy.The algorithm was applied to recognition of abnormal data of surrounding rock deformation of under-ground cavern in Yebatan Hydropower Station.It was proved that the algorithm can effectively avoid the problems of gradient ex-plosion,disappearance and time-consuming,which greatly improved the efficiency and recognition rate of abnormal value analy-sis.Relevant experiences can be used as reference in the recognition of abnormal monitoring data of similar projects.

吴忠明;李天述;张波;周明;张瀚;周靖人

浙江华东测绘与工程安全技术有限公司,浙江杭州 311122中国电建集团华东勘测设计研究院有限公司,浙江杭州 311122四川大学山区河流开发保护全国重点实验室,四川 成都 610065||四川大学水利水电学院,四川成都 610065

水利科学

异常数据识别地下洞室深度学习时域卷积神经网络标准自适应

abnormal data recognitionunderground caverndeep learningtemporal convolutional network(TCN)criterion adaptation

《人民长江》 2024 (008)

216-221 / 6

四川省科技厅重点研发项目(2022YFS0535)

10.16232/j.cnki.1001-4179.2024.08.029

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