LAUSR

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.