基于海量数据融合的设备状态评价方法
发布时间：2018-09-11 07:54
【摘要】：针对传统方法难以实现对海量数据环境下的设备状态评价的问题,提出了一种基于海量数据融合的设备状态评价方法。首先,利用擅长处理海量数据的分布式聚类算法K-means对海量状态数据进行预处理为多个簇,并求出各个簇的质心作为该簇的代表信息;然后对代表信息进行加权处理;最后利用证据理论对加权的代表信息进行融合,从而决断出设备的状态。通过仿真实验结果表明,该方法能对海量信息进行有效融合,并能更合理地决断出设备的状态信息。
[Abstract]:Aiming at the problem that traditional methods are difficult to evaluate the equipment status in mass data environment, a method of equipment status evaluation based on mass data fusion is proposed. Firstly, the distributed clustering algorithm K-means which is good at processing mass data is used to preprocess mass state data into multiple clusters, and the centroid of each cluster is obtained. Finally, the weighted representative information is fused with the evidence theory to determine the state of the device. The simulation results show that the method can effectively fuse the massive information and reasonably determine the state information of the device.
【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;
【基金】：国家自然科学基金资助项目(51475097) 贵州省基础研究重大项目(黔科合JZ字[2014]2001)
【分类号】：TH17
本文编号：2236077
[Abstract]:Aiming at the problem that traditional methods are difficult to evaluate the equipment status in mass data environment, a method of equipment status evaluation based on mass data fusion is proposed. Firstly, the distributed clustering algorithm K-means which is good at processing mass data is used to preprocess mass state data into multiple clusters, and the centroid of each cluster is obtained. Finally, the weighted representative information is fused with the evidence theory to determine the state of the device. The simulation results show that the method can effectively fuse the massive information and reasonably determine the state information of the device.
【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;
【基金】：国家自然科学基金资助项目(51475097) 贵州省基础研究重大项目(黔科合JZ字[2014]2001)
【分类号】：TH17
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