云加端的嵌套滑动窗口故障信号在线检测方法研究
发布时间：2018-08-06 13:15
【摘要】：针对当前制造业生产线设备的故障检测效率以及检测方法通用性不高的问题,提出了一种云端融合的动态嵌套滑动窗口故障信号在线检测算法。该算法采用云服务与智能终端在线检测相结合的架构,利用云服务大存储量和高计算速度、精度的优势,解决了终端设备对故障信号处理能力不足以及仅能对线上数据进行单次扫描的问题。云计算中心根据数据流的波动情况初步确定滑动窗口大小,再根据对异常信号的判断,向智能终端反馈故障信号的大小和相对位置,通过动态嵌套滑动窗口对其进行定位。理论分析和实验结果表明,该方法对周期信号有较好的通用性,而且有效提高了故障检测的效率。
[Abstract]:In order to solve the problem that the fault detection efficiency of manufacturing line equipment is not high and the detection method is not universal, a dynamic nested sliding window fault signal on-line detection algorithm based on cloud fusion is proposed. The algorithm adopts the framework of cloud service and intelligent terminal on-line detection, and makes use of the advantages of large storage capacity, high computing speed and precision of cloud service. It solves the problem that the terminal equipment has insufficient ability to deal with the fault signal and can only scan the data on the line at a single time. Cloud computing center preliminarily determines the size of sliding window according to the fluctuation of data flow, then according to the judgement of abnormal signal, feedback the size and relative position of fault signal to intelligent terminal, and locate it by dynamically nested sliding window. The theoretical analysis and experimental results show that the proposed method has good generality for periodic signals and effectively improves the efficiency of fault detection.
【作者单位】： 贵州大学现代制造技术教育部重点实验室;贵州大学机械工程学院;贵州省公共大数据重点实验室;
【基金】：贵州省重大科技专项资助项目(黔科合重大专项字[2013]6019,黔科合重大专项字[2012]6018) 贵州大学研究生创新基金资助项目(研理工2017039);贵州大学面向智能装备领域的“技术众筹”研究生创新基地资助项目(JSZC[2016]004)
【分类号】：TP274;TP393.09
本文编号：2167849
[Abstract]:In order to solve the problem that the fault detection efficiency of manufacturing line equipment is not high and the detection method is not universal, a dynamic nested sliding window fault signal on-line detection algorithm based on cloud fusion is proposed. The algorithm adopts the framework of cloud service and intelligent terminal on-line detection, and makes use of the advantages of large storage capacity, high computing speed and precision of cloud service. It solves the problem that the terminal equipment has insufficient ability to deal with the fault signal and can only scan the data on the line at a single time. Cloud computing center preliminarily determines the size of sliding window according to the fluctuation of data flow, then according to the judgement of abnormal signal, feedback the size and relative position of fault signal to intelligent terminal, and locate it by dynamically nested sliding window. The theoretical analysis and experimental results show that the proposed method has good generality for periodic signals and effectively improves the efficiency of fault detection.
【作者单位】： 贵州大学现代制造技术教育部重点实验室;贵州大学机械工程学院;贵州省公共大数据重点实验室;
【基金】：贵州省重大科技专项资助项目(黔科合重大专项字[2013]6019,黔科合重大专项字[2012]6018) 贵州大学研究生创新基金资助项目(研理工2017039);贵州大学面向智能装备领域的“技术众筹”研究生创新基地资助项目(JSZC[2016]004)
【分类号】：TP274;TP393.09
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