Data validation of multifunctional sensors using independent and related variables

To enhance the reliability of multifunctional sensors, a novel data validation strategy is presented by handling independent and related variables separately. The maximal information coefficient (MIC), which can measure the strength of the correlation between two variables, is applied to divide all variables of multifunctional sensors into related and independent. For one thing, the k-nearest neighbor (kNN) rule is introduced to accomplish fault detection and isolation of independent variables, and the grey predictive model GM(1,1), which has the advantages of low computation burden and high accuracy, is adopted to achieve data recovery of faulty independent variables. For another, the kernel principal component analysis (KPCA), which can handle possible non-linearity of data, is employed to realize fault detection of related variables. An iterative reconstruction-based contribution (IRBC) method is developed to isolate all faulty related variables, and data recovery of them are implemented using a fuzzy similarity (FS)-based reconstruction method based on the spatial correlations among related variables. An experimental system for multifunctional sensors is built to evaluate the proposed strategy, and the performance comparisons with its counterparts are also conducted.