计算机工程与应用2016，Vol.52Issue(20)：103-107,176,6.DOI:10.3778/j.issn.1002-8331.1603-0074
基于Spark计算框架的高铁振动数据经验模态分解
Empirical mode decomposition of high-speed rail data based on Spark com-puting framework
摘要
Abstract
The security problem of high-speed railway has gained more and more attention. The vibration signals of the train operation are collected by the sensors installed on the high speed rail. The faults of high speed rail can be discovered by analyzing and processing the collected vibration data. The Empirical Mode Decomposition(EMD)is a method which decomposes nonlinear and non-stationary signals to a sum of several intrinsic mode functions, so it plays a vital role in the domains of signal analysis and processing. However, the volume of collected data is very big. Then the speed of signal decomposition becomes a bottleneck. This paper presents a parallelized EMD algorithm under Spark, a framework based on the distributed memory computing and Resilient Distributed Datasets(RDD). Then, the real data is employed to validate the proposed algorithm. Finally, the experimental results are analyzed by three indexes, e.g., Speedup, Sizeup and Scaleup. It is shown that the parallelized method has good effect on the three indexes, which indicates that it can provide a reliable solution for the decomposition of a large number of vibration signals.关键词
振动信号/经验模态分解/Spark/并行化Key words
vibration signals/Empirical Mode Decomposition(EMD)/Spark/parallelization分类
信息技术与安全科学引用本文复制引用
李明,李天瑞,陈志,杨燕..基于Spark计算框架的高铁振动数据经验模态分解[J].计算机工程与应用,2016,52(20):103-107,176,6.基金项目
国家自然科学基金（No.61573292）。 （）
