An Efficient Approach for Fault Detection, Isolation, and Data Recovery of Self-Validating Multifunctional Sensors

A novel fault detection, isolation, and data recovery (FDIR) approach for self-validating multifunctional sensors is presented in this paper. To improve the fault detection accuracy under multiple steady conditions for multifunctional sensors, a sparse non-negative matrix factorization (SNMF)-based model is employed to accomplish fault detection through a combination of newly proposed $C^{2}$ and squared prediction error (SPE) statistics. Furthermore, a self-adaptive multiple-variable reconstruction strategy (SMVR) is proposed to achieve high accuracy on multiple-fault isolation and data recovery for faulty sensitive units. The performance of the proposed approach is fully verified in a real experimental system for self-validating multifunctional sensors, and it is compared with those of other fault detection models, such as principal component analysis (PCA), non-negative matrix factorization (NMF), and fault isolation algorithms, such as PCA-based contribution plots and SNMF-based contribution plots. The experimental results demonstrate that the proposed approach provides an excellent solution to the FDIR of self-validating multifunctional sensors.