Feature selection for multivariate contribution analysis in fault detection and isolation

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. © 2020 The Franklin Institute