FINNIM: Iterative Imputation of Missing Values in Dissolved Gas Analysis Dataset

Missing values are a common occurrence in a number of real world databases, and statistical methods have been developed to deal with this problem, referred to as missing data imputation. In the detection and prediction of incipient faults in power transformers using dissolved gas analysis (DGA), the problem of missing values is significant and has resulted in inconclusive decision-making. This study proposes an efficient nonparametric iterative imputation method named FINNIM, which comprises of three components: 1) the imputation ordering; 2) the imputation estimator; and 3) the iterative imputation. The relationship between gases and faults, and the percentage of missing values in an instance are used as a basis for the imputation ordering; whereas the plausible values for the missing values are estimated from k-nearest neighbor instances in the imputation estimator, and the iterative imputation allows complete and incomplete instances in a DGA dataset to be utilized iteratively for imputing all the missing values. Experimental results on both artificially inserted and actual missing values found in a few DGA datasets demonstrate that the proposed method outperforms the existing methods in imputation accuracy, classification performance, and convergence criteria at different missing percentages.