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

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