Measurement Accuracy of the Electrosensitive Protective Device Response Time When Using the Double Penetration Method

The Double Penetration Method (DPM) method of measuring ESPD (Electrosensitive Protective Device) response time was presented by Dzwiarek (1997). Calibrating the measuring equipment is a crucial stage of the procedure. Experimental verification of theoretical predictions is also crucial. For calibration purposes, a device simulating real ESPD operation thus enabling a correct setting of the response time was designed (Dzwiarek, 1997).

Theoretical analysis has shown that measuring ESPD response time with the DPM is subject to localisation errors made in the localisation of the detection zone border, rod position measurement errors made during highspeed penetration, and time delay measurement errors. The values of all those components of the total error have been determined experimentally using the calibrating device. Measurements have been taken under conditions as close to real ones as possible proving that the total measurement error is really enclosed within the assumed limits.     

基于AVMD与DPC-FCM的旋转机械无监督故障诊断方法

目的 针对旋转机械故障诊断过程中存在故障信号特征提取困难、故障诊断过程有标签数据较少、故障诊断准确率低等问题,提出自适应变分模态分解算法(Adaptive Variational Mode Decomposition, AVMD)与密度峰值算法优化的模糊C均值算法(Clustering by Fast Search and Find of Density Peaks Optimizing Fuzzy C-Means,DPC-FCM)结合的无监督诊断方法。方法 首先,将多尺度排列熵与峭度相结合的综合系数作为适应度函数,对VMD算法的惩罚因子alpha和模态个数K进行参数寻优,提取分解后本征模态函数(Intrinsic Mode Function,IMF)的平均样本熵与平均模糊熵,并输入至聚类算法中。其次,提出利用密度峰值聚类算法确定FCM的初始聚类中心,降低聚类结果的随机性。结果 将提出的无监督故障诊断模型应用到滚动轴承试验信号中,实现了准确的故障诊断。结论 AVMD在故障提取方面具有优越性,同时DPC算法可以有效提高FCM算法无监督聚类的准确性,二者结合可以有效实现旋转机械故障的智能分类。     

矿山机电设备运行大数据应用研究

以矿山机电设备运行数据采集为切入点研究矿山大数据技术应用.利用传感器采集数据,基于LoRaWAN物联网远程通信接入平台传输采集数据,构建起矿山机电设备状态数据采集系统,以大数据技术应用理念出发,设计与分析矿山机电设备云计算服务平台,实现对矿山设备运行状态的实时监测.通过对矿山设备工作数据的积累,结合矿山信息化系统的数据...     

制作多媒体课件提高电机拖动基础课程的教学效果

通过制作多媒体课件,探讨提高《电机拖动基础》课程教学效果的尝试及现代化教学手段对教学影响的重要性、必要性和实际运用中需要注意的问题。