提出一个两步信息提取方法用于更精确地捕获性能指标相关的信息.在第一步中,根据皮尔森相关系数,所有的过程变量被分成两个集合.其中一个由与特定指标强相关的过程变量组成,另一个集合由特定指标弱相关过程变量组成.随后,在两个集合中执行PCA分解,潜变量的方向不同于原始的过程变量.也就是说,原本强相关变量集合中的潜变量与特定指标的相关性可能会变弱.然而,弱相关变量集合中的潜变量与特定指标的相关性可能会变强.因此,在第二步中,根据皮尔森相关系数,两个集合被进一步从潜变量的角度分解为四个子集.得到的两个与特定指标强相关的子集构成与特定指标相关的子空间.接着,一种基于T2统计量和预测的特定指标的混合监控策略被提出用于特定指标相关的过程监测.最后,提出的方法被应用于TE过程,结果显示了所提方法的有效性.%A two-step information extraction method is presented to capture the specific index-related information more accurately. In the first step, the overall process variables are separated into two sets based on Pearson correlation coefficient. One is process variables strongly related to the specific index and the other is process variables weakly related to the specific index. Through performing principal component analysis (PCA) on the two sets, the directions of latent variables have changed. In other words, the correlation between latent variables in the set with strong correlation and the specific index may become weaker. Meanwhile, the correlation between latent variables in the set with weak correlation and the specific index may be enhanced. In the second step, the two sets are further divided into a subset strongly related to the specific index and a subset weakly related to the specific index from the perspective of latent variables using Pearson correlation coefficient, respectively. Two subsets strongly related to the specific index form a new subspace related to the specific index. Then, a hybrid monitoring strategy based on predicted specific index using partial least squares (PLS) and T2 statistics-based method is proposed for specific index-related process monitoring using comprehensive information. Predicted specific index reflects real-time information for the specific index. T2 statistics are used to monitor specific index-related information. Finally, the proposed method is applied to Tennessee Eastman (TE). The results indicate the effectiveness of the proposed method.
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