Quantum mechanics-based missing value estimation framework for industrial data

초록

As the importance of data-based predictive maintenance frameworks is rising, one of the emerging issues is the missing values in industrial data. Data with various missing values due to measurement sensor faults and power change issues can not only cause errors in future analyses but also lead to data shortages. Although various missing value estimation methods have been proposed to address this issue, correction methods that consider the fine uncertainty and noise of the missing values themselves have been relatively unexplored. Therefore, this study modeled missing values estimated using Gaussian progress regression by considering the fine movements and noises of data using a quantum mechanics-based stochastic differential equation and corrected them using Ito's lemma. Estimating the missing data in this manner more closely simulates the attributes of data compared to existing estimation methods, enabling more accurate analysis. To demonstrate the excellence of the proposed framework, missing values were estimated using vehicle operation data and semiconductor manufacturing data with multiple missing values, and the estimation results were compared with those of existing algorithms.

키워드