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Vol. 6 No. 1 (2025)
2025 Edition

Articles

A Comparative Study on Machine Learning-based Data Quality Assessment Methods for Industrial Time Series

Published 2025-09-24

  • Xin Wen
    • ,
  • Wenxuan Zheng
    • ,
  • Haodong Zhang

Xin Wen
Applied Data Science, University of Southern California, CA, USA

Wenxuan Zheng
Applied Math, University of California, Los Angeles, CA, USA

Haodong Zhang
Computer Science, New York University, NY, USA

Abstract

Industrial time series data quality assessment represents a critical challenge in modern manufacturing environments where sensor networks generate vast amounts of temporal data. This paper presents a comprehensive comparative analysis of machine learning algorithms applied to data quality evaluation in industrial settings. The study examines five distinct machine learning approaches including Random Forest, Support Vector Machines, Long Short-Term Memory networks, Gradient Boosting Machines, and Convolutional Neural Networks for their effectiveness in detecting anomalies, missing values, and noise patterns in industrial sensor data. Through extensive experiments on three industrial datasets comprising temperature, pressure, and vibration measurements from manufacturing processes, we evaluate each algorithm's performance using precision, recall, F1-score, and computational efficiency metrics. Results demonstrate that ensemble methods achieve superior accuracy rates of 94.3% for anomaly detection tasks while deep learning approaches exhibit better generalization capabilities across diverse data patterns. The findings provide practical guidance for selecting appropriate machine learning techniques based on specific industrial data characteristics and quality assessment requirements.

Keywords

Data Quality Assessment, Machine Learning, Industrial Time Series, Anomaly Detection

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