The purpose of this research is to investigate the factors which affect the performance of the loading and hauling equipment at Skorpion zinc mine, Namibia and also to find possible solutions to eliminate them, so tha...The purpose of this research is to investigate the factors which affect the performance of the loading and hauling equipment at Skorpion zinc mine, Namibia and also to find possible solutions to eliminate them, so that the weekly Zinc Oxide and ex-pit waste tonnages required could be produced. This is due to the high demand of Zinc on the market. The investigation on road conditions was focused on the effects of rolling resistance, grade resistance and road widths from the road between bench 540 in pit 103 to the Zinc oxide medium grade stockpile. Cycle time data were obtained by time and motion study of the load/haul/dump cycle from bench 540 loading site to the stockpile. The data used for equipment matching by queuing theory (excel modelling) was obtained when the loaders where loading Arkose at different loading sites. The effects of different weather conditions i.e. mist, rain and wind on production where determined by collecting actual shift production tonnages and comparing with target shift production targets during this conditions. The results produced from time and motion studies show that the haul trucks have an average availability of 80.4% and utilization at 49.7% which are very low when compared to the benchmark value of 89% and 69% for availability and utilization respectively. Decline in performance time is caused by factors such as daily safety meetings, lunch breaks, blasting, tools break down and the daily machine service. Rolling resistance is also one of the factors affecting production time at the mine. The rolling resistance of different segments was determined by roughness defect scores (RDS). From calculations, it is clearly seen that if the RR could be reduced to 2% on every road segment, then each cycle period per truck can be reduced by 1.24 minutes. This will increase the production of the haulers and decrease the operating cost. It was recommended that the wearing course of the road surface be treated with a bitumen based dust suppression product in order to keep the surface’s rolling resistance to an absolute minimum (i.e. RR = 2%) [1]. The current average hauling road width is 15.987 m while the correct road width at Skorpion Zinc Mine must be 21.35 m to prevent bunching of trucks. It is therefore recommended that the haul roads be widened to 21.35 m width per segment.展开更多
红外技术能有效地检测电力设备过热缺陷,具有远距离、不接触、不取样、准确、快速、直观等特点。传统的电力设备故障红外人工诊断耗时、耗力,而针对人工诊断不足提出的智能诊断其难点之一在于能否较好的获得感兴趣区域(ROI,Region of in...红外技术能有效地检测电力设备过热缺陷,具有远距离、不接触、不取样、准确、快速、直观等特点。传统的电力设备故障红外人工诊断耗时、耗力,而针对人工诊断不足提出的智能诊断其难点之一在于能否较好的获得感兴趣区域(ROI,Region of interest)。红外图像具有强度集中、对比度低等性质,常用的分割算法用于电力设备红外图像ROI获取,其结果往往是过分割。针对过分割难点,本文提出一种基于FAs T-Match算法的电力设备红外图像分割方法。首先,运用FAs T-Match算法在可见光图像中近似模板匹配,然后在红外与可见光图像之间通过近似仿射变换找到目标在红外图像中的近似区域,最后用分割算法对近似区域分割。实验结果表明,提出的方法能够较好地解决电力设备红外图像过分割问题。展开更多
针对现阶段用电设备状态监测技术存在的处理速度较慢、准确率较低等问题,文中基于多突变点检测和模板匹配策略提出了一种用电设备在线状态监测方法。该方法在缓冲区模型和滑动窗口模型的基础上,利用多路搜索树突变点检测(Ternary Search...针对现阶段用电设备状态监测技术存在的处理速度较慢、准确率较低等问题,文中基于多突变点检测和模板匹配策略提出了一种用电设备在线状态监测方法。该方法在缓冲区模型和滑动窗口模型的基础上,利用多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Smirnov,TSTKS)算法形成窗口维度和缓冲区维度的特征向量,通过两种维度的模板匹配实现用电设备的运行状态匹配和状态切换时刻定位。基于家用电冰箱的仿真实验结果表明,所提方法具有检测速度快、准确率高等优点,可为用电设备状态监测领域提供参考。展开更多
文摘The purpose of this research is to investigate the factors which affect the performance of the loading and hauling equipment at Skorpion zinc mine, Namibia and also to find possible solutions to eliminate them, so that the weekly Zinc Oxide and ex-pit waste tonnages required could be produced. This is due to the high demand of Zinc on the market. The investigation on road conditions was focused on the effects of rolling resistance, grade resistance and road widths from the road between bench 540 in pit 103 to the Zinc oxide medium grade stockpile. Cycle time data were obtained by time and motion study of the load/haul/dump cycle from bench 540 loading site to the stockpile. The data used for equipment matching by queuing theory (excel modelling) was obtained when the loaders where loading Arkose at different loading sites. The effects of different weather conditions i.e. mist, rain and wind on production where determined by collecting actual shift production tonnages and comparing with target shift production targets during this conditions. The results produced from time and motion studies show that the haul trucks have an average availability of 80.4% and utilization at 49.7% which are very low when compared to the benchmark value of 89% and 69% for availability and utilization respectively. Decline in performance time is caused by factors such as daily safety meetings, lunch breaks, blasting, tools break down and the daily machine service. Rolling resistance is also one of the factors affecting production time at the mine. The rolling resistance of different segments was determined by roughness defect scores (RDS). From calculations, it is clearly seen that if the RR could be reduced to 2% on every road segment, then each cycle period per truck can be reduced by 1.24 minutes. This will increase the production of the haulers and decrease the operating cost. It was recommended that the wearing course of the road surface be treated with a bitumen based dust suppression product in order to keep the surface’s rolling resistance to an absolute minimum (i.e. RR = 2%) [1]. The current average hauling road width is 15.987 m while the correct road width at Skorpion Zinc Mine must be 21.35 m to prevent bunching of trucks. It is therefore recommended that the haul roads be widened to 21.35 m width per segment.
文摘红外技术能有效地检测电力设备过热缺陷,具有远距离、不接触、不取样、准确、快速、直观等特点。传统的电力设备故障红外人工诊断耗时、耗力,而针对人工诊断不足提出的智能诊断其难点之一在于能否较好的获得感兴趣区域(ROI,Region of interest)。红外图像具有强度集中、对比度低等性质,常用的分割算法用于电力设备红外图像ROI获取,其结果往往是过分割。针对过分割难点,本文提出一种基于FAs T-Match算法的电力设备红外图像分割方法。首先,运用FAs T-Match算法在可见光图像中近似模板匹配,然后在红外与可见光图像之间通过近似仿射变换找到目标在红外图像中的近似区域,最后用分割算法对近似区域分割。实验结果表明,提出的方法能够较好地解决电力设备红外图像过分割问题。
文摘针对现阶段用电设备状态监测技术存在的处理速度较慢、准确率较低等问题,文中基于多突变点检测和模板匹配策略提出了一种用电设备在线状态监测方法。该方法在缓冲区模型和滑动窗口模型的基础上,利用多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Smirnov,TSTKS)算法形成窗口维度和缓冲区维度的特征向量,通过两种维度的模板匹配实现用电设备的运行状态匹配和状态切换时刻定位。基于家用电冰箱的仿真实验结果表明,所提方法具有检测速度快、准确率高等优点,可为用电设备状态监测领域提供参考。
