Yan Zhengbing, Kuang Te-Hui, Yao Yuan
College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China.
Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.
ISA Trans. 2017 Sep;70:389-399. doi: 10.1016/j.isatra.2017.06.014. Epub 2017 Jun 28.
In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis.
近年来，间歇过程的多变量统计监测已成为一个热门研究课题，其中多变量故障隔离是一个重要步骤，旨在识别对检测到的过程异常贡献最大的故障变量。尽管贡献图已在统计故障隔离中普遍使用，但此类方法存在相关变量之间的涂抹效应问题。特别是在间歇过程监测中，变量轨迹中存在的高自相关性和互相关性使得涂抹效应不可避免。为解决这一问题，本研究提出了一种基于变量选择的故障隔离方法，该方法将故障隔离问题转化为偏最小二乘判别分析中的变量选择问题，并通过计算稀疏偏最小二乘模型来解决。与传统方法不同，所提出的方法强调每个过程变量的相对重要性。此类信息可能有助于过程工程师进行根本原因诊断。
