This paper presents a multivariate linear contribution analysis in the context of fault detection, isolation and diagnosis. The usually univariate contribution analysis in fault isolation is improved by the use of feature selection. The fault index and the individual contributions of the variables are calculated by Probabilistic Principal Component Analysis. A new and more efficient method is proposed to select the most decisive variables that contribute to the fault. Experiments are conducted with illustrative synthetic benchmarks and the Tennessee Eastman chemical plant simulator. Among the multivariate selection searches, the Sequential Backward and Forward search shows an optimized equilibrium between the quality of the selected set of contributing variables and the computational burden, compared to an exhaustive and Branch & Bound search.

本文提出了在故障检测，隔离和诊断的背景下的多元线性贡献分析。通过使用特征选择，可以改善故障隔离中通常的单变量贡献分析。通过概率主成分分析计算故障指数和变量的各个贡献。提出了一种新的，更有效的方法来选择对故障有影响的决定性变量。实验是使用说明性的合成基准和田纳西伊士曼化工厂模拟器进​​行的。与穷举搜索和分支定界搜索相比，在多元选择搜索中，顺序向后搜索和正向搜索显示出所选的一组贡献变量的质量与计算负担之间的最佳平衡。