当前期刊: Big Data Research Go to current issue    加入关注   
显示样式:        排序: 导出
我的关注
我的收藏
您暂时未登录!
登录
  • A Hierarchical Dimension Reduction Approach for Big Data with Application to Fault Diagnostics
    Big Data Res. (IF 2.952) Pub Date : 2019-08-22
    R. Krishnan; V.A. Samaranayake; S. Jagannathan

    About four zetta bytes of data, which falls into the category of big data, is generated by complex manufacturing systems annually. Big data can be utilized to improve the efficiency of an aging manufacturing system, provided, several challenges are handled. In this paper, a novel methodology is presented to detect faults in manufacturing systems while overcoming some of these challenges. Specifically, a generalized distance measure is proposed in conjunction with a novel hierarchical dimension reduction (HDR) approach. It is shown that the HDR can tackle challenges that are frequently observed during distance calculation in big data scenarios, such as norm concentration, redundant dimensions, and a non-invertible correlation matrices. Subsequently, a probabilistic methodology is developed for isolation and detection of faults. Here, Edgeworth expansion based expressions are derived to approximate the density function of the data. The performance of the dimension reduction methodology is demonstrated to be efficient with simulation results involving the use of big data sets. It is shown that HDR is able to explain almost 90% of the total information. Furthermore, the proposed dimension reduction methodology is seen to outperform standard dimension reduction approaches and is able to improve the performance of standard classification methodologies in high dimensional scenarios.

    更新日期:2020-01-04
Contents have been reproduced by permission of the publishers.
导出
全部期刊列表>>
2020新春特辑
限时免费阅读临床医学内容
ACS材料视界
科学报告最新纳米科学与技术研究
清华大学化学系段昊泓
自然科研论文编辑服务
中国科学院大学楚甲祥
上海纽约大学William Glover
中国科学院化学研究所
课题组网站
X-MOL
北京大学分子工程苏南研究院
华东师范大学分子机器及功能材料
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug