Abstract: Partial Least Squares（PLS） has been widely used in multivariate statistical process monitoring methods for industrial processes, and it is computation-intensive and time-demanding when dealing with massive data. To solve this problem to consider time complexity, a novel implementation of parallel partial least squares is proposed using MapReduce, which consists of the parallelization of cross validation. Using Tennessee-Eastman Process data as an example, experiments are conducted on a Hadoop cluster, which is a collection of ordinary computers. The experimental results demonstrate that parallel partial least squares algorithm can handle massive process data, can significantly cut down the modeling time, and gains a basically linear speedup with the number of computers increased, and can be easily scaled up.

Key words: cloud computing, process monitoring, MapReduce, partial least squares, parallel algorithm

摘要： 偏最小二乘算法（PLS）是现代工业过程常用的多变量统计过程监控方法之一，然而在现代工业背景下，采用单台PC对大规模工业过程数据进行PLS回归分析的时间复杂度较高。针对此问题，在Hadoop云平台上提出了一种基于MapReduce框架的并行PLS算法。从时间复杂度考虑，将其交叉有效性检验部分并行处理。在三台PC上搭建三个节点的Hadoop全分布集群平台上，以田纳西-伊斯曼过程仿真平台数据回归分析为例，验证所提出的算法。实验结果表明，在使用PLS做现代大规模工业过程数据分析时，所提出的算法在保证精度的前提下，能有效改善数据处理的时效性并且随着PC数量的增加时效性具有近似线性的提高。

关键词: 云计算, 过程监控, MapReduce, 偏最小二乘算法, 并行算法