基于安全监测的水库岸坡PHM系统框架及故障预测技术研究
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摘要
水库岸坡故障是影响水库安全稳定运行的重要因素之一,对水库岸坡进行故障预测是保证水库安全运营、延长寿命周期的重要手段。现将先进的PHM系统引入水库岸坡中,研究建立水库岸坡PHM系统框架,根据安全监测资料,选取时间、库水位、库水位的变化速率、累计降雨量等特征参数作为岸坡位移的影响因子,采用BP神经网络进行模型训练,重点建立水库岸坡位移预测模型,预测岸坡位移变化趋势,实时掌握岸坡异常情况,以便在岸坡发生破坏前消除故障。案例分析表明,所建立的水库岸坡故障预测模型输出结果与实测结果吻合良好,可信度高,能有效满足水库岸坡故障预测实际需求。
Reservoir bank slope failure is one of the important factors that affect the safe and stable operation of the reservoir. It is an important means to ensure the safe operation of the reservoir and prolong the life cycle. This paper introduces the advanced PHM system into the bank slope,study on the framework of reservoir slope PHM system,according to safety monitoring data,selecting the time,reservoir water level,reservoir water level change rate,cumulative rainfall as an influencing factor of bank slope displacement. The BP neural network is used to train the model,the reservoir bank slope displacement prediction model is established,the trend of bank slope displacement is forecasted,the abnormality of bank slope is monitored,and the possible failure of the bank slope in the future mission time is predicted and judged. In order to eliminate the failure before the destruction of the bank slope. Case analysis shows thatthe results of the bank fault prediction model are in good agreement with the measured results,and the reliability is high,which can effectively meet the actual demand of reservoir bank fault prediction.
Reservoir bank slope failure is one of the important factors that affect the safe and stable operation of the reservoir. It is an important means to ensure the safe operation of the reservoir and prolong the life cycle. This paper introduces the advanced PHM system into the bank slope,study on the framework of reservoir slope PHM system,according to safety monitoring data,selecting the time,reservoir water level,reservoir water level change rate,cumulative rainfall as an influencing factor of bank slope displacement. The BP neural network is used to train the model,the reservoir bank slope displacement prediction model is established,the trend of bank slope displacement is forecasted,the abnormality of bank slope is monitored,and the possible failure of the bank slope in the future mission time is predicted and judged. In order to eliminate the failure before the destruction of the bank slope. Case analysis shows thatthe results of the bank fault prediction model are in good agreement with the measured results,and the reliability is high,which can effectively meet the actual demand of reservoir bank fault prediction.
引文
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[2]段功豪,牛瑞卿,赵艳南,等.基于动态指数平滑模型的降雨诱发型滑坡预测[J].武汉大学学报:信息科学版,2016(7):958-962.
[3]林庆.区域滑坡灾害危险性评价研究[D].重庆:重庆交通大学,2016.
[4]彭宇,刘大同,彭喜元.故障预测与健康管理技术综述[J].电子测量与仪器学报,2010(1):1-9.
[5]蓝祝光,黄铭.基于实测信息的海堤PHM系统框架及关键技术研究[J].水土保持通报,2017(3):307-313.
[6]杨怀志.高速铁路大型桥梁养护维修PHM系统应用初探[J].铁道建筑,2017(6):12-16,35.
[7]时旺,孙宇锋,王自力,等.PHM系统及其故障预测模型研究[J].火力与指挥控制,2009(10):29-32,35.
[8]张万英.基于飞行数据的故障预测与健康管理系统研究[D].青岛:青岛科技大学,2014.
[9]曾声奎,Michael G.Pecht,吴际.故障预测与健康管理(PHM)技术的现状与发展[J].航空学报,2005(5):626-632.
[10]赵建辉,赵建平,孙永江,等.BP神经网络在雷达故障诊断中的应用[J].兵工自动化,2013(2):37-39,44.
[11]张国栋,李泯蒂,龙海涛,等.水库型滑坡稳定性动态变化过程有限元分析[J].水利水电技术,2016(2):104-109.
[12]宋丹青,宋宏权.库水位升降作用下库岸滑坡稳定性研究[J].东北大学学报:自然科学版,2017(5):735-739.