This review examines the role of industrial data in enabling artificial intelligence (AI) technologies within the framework of Industry 4.0. Key aspects of industrial data management, including collection, preprocessing, integration, and utilization for training AI models, are analyzed and systematically categorized. Criteria for assessing data quality are defined, covering accuracy, completeness, consistency, and confidentiality, and practical recommendations are proposed for preparing data for effective machine learning and deep learning applications. In addition, current approaches to data management are compared, and methods for evaluating and improving data quality are outlined. Particular attention is given to challenges and limitations in industrial contexts, as well as the prospects for leveraging high-quality data to enhance AI-driven smart manufacturing.
This review examines the role of industrial data in enabling artificial intelligence (AI) technologies within the framework of Industry 4.0. Key aspects of industrial data management, including collection, preprocessing, integration, and utilization for training AI models, are analyzed and systematically categorized. Criteria for assessing data quality are defined, covering accuracy, completeness, consistency, and confidentiality, and practical recommendations are proposed for preparing data for effective machine learning and deep learning applications. In addition, current approaches to data management are compared, and methods for evaluating and improving data quality are outlined. Particular attention is given to challenges and limitations in industrial contexts, as well as the prospects for leveraging high-quality data to enhance AI-driven smart manufacturing. Read More
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