摘要
采用自适应的变分模态分解(adaptive variational modal decomposition,简称AVMD)与核极限学习机(kernel extreme learning machine,简称KELM)联合的方法对水闸在泄流过程中的监测信号进行振动预测分析,用以辅助决策和及时预警。首先,基于互信息准则确定AVMD的分解模态数,克服变分模态分解(variatronal modal decomposition,简称VMD)盲目选取分解参数的缺点,利用AVMD把水闸振动信号分解成K个固态模量(intrinsic mode function,简称IMF);其次,通过KELM对各IMF分量分别进行预测;最后,将各测点对应的IMFs预测结果相加作为最终的预测值。结合某水闸在自由泄流工况下的振动数据,分别采用AVMD-KELM和KELM模型、支持向量机(support vector machine,简称SVM)模型对其振动趋势进行预测,并将预测结果进行对比分析。结果表明,AVMD-KELM模型得到的预测结果与实测值更加接近,计算速度更快,精度更高,且误差较小,该方法可有效预测水闸结构的振动趋势。
An adaptive variational modal decomposition(AVMD)combined with kernel extreme learning machine(KELM)is used to predict the vibration of the sluice in the discharge process,which is used to assist decision-making and early warning.Firstly,the decomposition modal number of AVMD is determined based on mutual information criterion to overcome the disadvantage of blindly selecting decomposition parameters of variatronal modal decomposition(VMD).AVMD is used to decompose on-line monitoring vibration sequence of sluice into several IMFs,which is used as input and output of KELM model.Secondly,each component is predicted separately by KELM,the hidden layer does not need to be artificially set and the output weights are calculated using the least squares method.Finally,the prediction results of IMFs corresponding to each measurement point are reconstructed as the final predicted value.Combined with the on-line monitoring data of Yucao Sluice under the condition of free discharge,the AVMD-KELM and KELM models and SVM models are used to predict the vibration trend,and the forecast results are compared and analyzed.The results show that the predicted results of AVMD-KELM modal are closer to the measured values,the calculation speed is faster,the accuracy is higher,and the error is smaller.The method can effectively predict the vibration trend of sluice structure.
作者
张建伟
华薇薇
侯鸽
赵瑜
郭西方
ZHANG Jianwei;HUA Weiwei;HOU Ge;ZHAO Yu;GUO Xifang(School of Water Conservancy, North China University of Water Resources and Electric Power Zhengzhou, 450046, China;Collaborative Innovation Center of Water Resources Efficiency and Protection Engineering Zhengzhou, 450046 , China;Henan Provincial Hydraulic Structure Safety Engineering Research Center Zhengzhou, 450046 , China;Henan Zhongyuan the Yellow River Engineering Company Limited Xinxiang, 453000, China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2019年第5期947-952,1128,共7页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目（51679091）
河南省高校科技创新人才计划资助项目（18HASTIT012）
广东省水利科技创新基金资助项目（2017-16）
华北水利水电大学研究生教育创新计划基金资助项目（YK2017-03）
关键词
自适应变分模态分解
核极限学习机
水闸
振动预测
adaptive variational modal decomposition
kernel extreme learning machine
sluice
vibration prediction
