고비용 공정데이터 획득 환경에서 불완비 공정데이터의 효율적인 결측치 대체 방법
An Efficient Method for Imputing Missing Values in Incomplete Process Data from High-Cost Data Acquisition Environments
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This study addresses the challenge of imputing missing values in incomplete process data collected from high-cost data acquisition environments. Such missingness arises due to insufficient completeness, accuracy, and consistency, which significantly affect the quality of critical-to-quality (CTQ) attributes in manufacturing processes. We systematically evaluate three state-of-the-art imputation methods— Multiple Imputation by Chained Equations (MICE), the machine learning-based missForest algorithm, and a deep learn- ing-based one-dimensional convolutional neural network (1D-CNN)— using real-world industrial data. Our analysis aims to identify the most effective imputation technique for handling complex and noisy process datasets typical in manufacturing settings. The results highlight the strengths and limitations of each method, providing practical guidance for selecting appropriate imputation approaches to improve the reliability of quality prediction and decision-making in industrial applications.
Abstract
This study addresses the challenge of imputing missing values in incomplete process data collected from high-cost data acquisition environments. Such missingness arises due to insufficient completeness, accuracy, and consistency, which significantly affect the quality of critical-to-quality (CTQ) attributes in manufacturing processes. We systematically evaluate three state-of-the-art imputation methods— Multiple Imputation by Chained Equations (MICE), the machine learning-based missForest algorithm, and a deep learn- ing-based one-dimensional convolutional neural network (1D-CNN)— using real-world industrial data. Our analysis aims to identify the most effective imputation technique for handling complex and noisy process datasets typical in manufacturing settings. The results highlight the strengths and limitations of each method, providing practical guidance for selecting appropriate imputation approaches to improve the reliability of quality prediction and decision-making in industrial applications.