断路器操动机构分合闸线圈电流波形对分析机构已有或潜在机械故障有重要参考意义,由于受外界环境的影响,许多算法在数据采集及波形特征点提取一致性方面存在一定的不足。文中利用零偏置计算、改进后的均值滤波算法对采集到的原始电流波...断路器操动机构分合闸线圈电流波形对分析机构已有或潜在机械故障有重要参考意义,由于受外界环境的影响,许多算法在数据采集及波形特征点提取一致性方面存在一定的不足。文中利用零偏置计算、改进后的均值滤波算法对采集到的原始电流波形数据进行预处理以降低外界因素的影响,同时针对不同的特征点,依据其在波形中的特征进行分类,研究相应的特征点提取算法;在此基础上,搭建了断路器分合闸线圈电流信号采集处理系统并完成了相应算法软件的开发,且以某550 k V断路器配套的液压碟簧操动机构为测试样机对算法进行了验证,获得了一致性较高的波形特征值。展开更多
The efficient processing of large amounts of data collected by the microseismic monitoring system(MMS),especially the rapid identification of microseismic events in explosions and noise,is essential for mine disaster ...The efficient processing of large amounts of data collected by the microseismic monitoring system(MMS),especially the rapid identification of microseismic events in explosions and noise,is essential for mine disaster prevention.Currently,this work is primarily performed by skilled technicians,which results in severe workloads and inefficiency.In this paper,CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multichannel microseismic signal waveforms.First,data collected by MMS was generated into 6-channel original waveforms based on events.After that,sample data sets of microseismic events,blasts,drillings,and noises were established through manual identification.These datasets were split into training sets and test sets according to a certain proportion,and transfer learning was performed on AlexNet,GoogLeNet,and ResNet50 pre-training network models,respectively.After training and tuning,optimal models were retained and compared with support vector machine classification.Results show that transfer learning models perform well on different test sets.Overall,GoogLeNet performed best,with a recognition accuracy of 99.8%.Finally,the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.展开更多
文摘断路器操动机构分合闸线圈电流波形对分析机构已有或潜在机械故障有重要参考意义,由于受外界环境的影响,许多算法在数据采集及波形特征点提取一致性方面存在一定的不足。文中利用零偏置计算、改进后的均值滤波算法对采集到的原始电流波形数据进行预处理以降低外界因素的影响,同时针对不同的特征点,依据其在波形中的特征进行分类,研究相应的特征点提取算法;在此基础上,搭建了断路器分合闸线圈电流信号采集处理系统并完成了相应算法软件的开发,且以某550 k V断路器配套的液压碟簧操动机构为测试样机对算法进行了验证,获得了一致性较高的波形特征值。
基金the National Key R&D Program of China(No.2021YFC2900500).
文摘The efficient processing of large amounts of data collected by the microseismic monitoring system(MMS),especially the rapid identification of microseismic events in explosions and noise,is essential for mine disaster prevention.Currently,this work is primarily performed by skilled technicians,which results in severe workloads and inefficiency.In this paper,CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multichannel microseismic signal waveforms.First,data collected by MMS was generated into 6-channel original waveforms based on events.After that,sample data sets of microseismic events,blasts,drillings,and noises were established through manual identification.These datasets were split into training sets and test sets according to a certain proportion,and transfer learning was performed on AlexNet,GoogLeNet,and ResNet50 pre-training network models,respectively.After training and tuning,optimal models were retained and compared with support vector machine classification.Results show that transfer learning models perform well on different test sets.Overall,GoogLeNet performed best,with a recognition accuracy of 99.8%.Finally,the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.
