New sensor fault detection and isolation strategy-based interval-valued data

In this paper, a new data-driven sensor fault detection and isolation (FDI) technique for interval-valued data is developed. The developed approach merges the benefits of generalized likelihood ratio (GLR) with interval-valued data and principal component analysis (PCA). This paper has three main contributions. The first contribution is to develop a criterion based on the variance of interval-valued reconstruction error to select the number of principal components to be kept in the PCA model. Secondly, interval-valued residuals are generated, and a new fault detection chart-based GLR is developed. Lastly, an enhanced interval reconstruction approach for fault isolation is developed. The proposed strategy is applied for distillation column process monitoring and air quality monitoring network.