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.

Key words: vibration signals, Empirical Mode Decomposition（EMD）, Spark, parallelization

摘要： 高铁的安全问题越来越受到人们关注，通过安装在高铁上的传感器可以采集到列车运行过程中的振动信号。分析和处理采集到的振动信号，可以对列车运行过程中出现的故障进行诊断。经验模态分解（EMD）适用于将非线性非平稳的信号分解为若干个固有模态函数之和，它在信号分析和处理领域起着至关重要的作用。但列车在不断运行过程中采集的数据量非常大，信号处理的速度成为了瓶颈。因此，借助大数据处理框架Spark基于分布式的内存运算、弹性式分布式数据集等特点，提出了基于Spark的并行化EMD算法，并利用实际数据进行算法评测，通过Speedup、Sizeup、Scaleup三个指标对实验结果进行分析，得到该并行化方法在三个指标上都有良好的效果，表明该算法可以为大量的振动信号分解提供可靠的解决方案。

关键词: 振动信号, 经验模态分解, Spark, 并行化