AI-POWERED PRODUCT DATA MANAGEMENT IN INDUSTRY 4.0: A BIBLIOGRAPHICAL ANALYSIS (original) (raw)

Predictive maintenance (PdM) solutions powered by data analytics and artificial intelligence (AI) have become more popular in today's dynamic industrial environment as a game-changing strategy to increase equipment life, operational effectiveness, and competitiveness. In order to understand the revolutionary impacts of artificial intelligence (AI), data analytics, and predictive maintenance on maintenance operations, this paper investigates the intricate relationships between these three technologies in the industrial sector. This research synthesizes current information, discovers gaps, and extracts insights crucial to grasping the evolving predictive maintenance environment via a thorough assessment of the literature from 2014 to 2024. The usefulness of numerous AI algorithms, such as logistic regression, support vector regression, random forests, neural networks, and linear regression, is analyzed in connection to predictive manufacturing. The study digs into multiple machine learning algorithms to evaluate which one is most effective for tackling predictive maintenance concerns in industrial contexts. Additionally, the research looks at optimization strategies to enhance the accuracy and usefulness of AI-driven maintenance predictions, employing data analytics insights for better maintenance scheduling. Real-time insights and predictive capabilities are offered by the integration of Big Data, IoT, and cyber-physical systems, which changes maintenance operations in the context of Industry 4.0. Experience-based, model-based, physics-based, data-driven, and hybrid techniques to PdM implementation are investigated, taking into consideration their respective demands and capabilities. Additionally, the research looks at how Industry 4.0 technologies-like robots, cloud computing, augmented reality, and IIoT-can aid with predictive maintenance duties. The research's results increase our knowledge of predictive maintenance in the context of Industry 4.0 and give practitioners, researchers, and industry stakeholders crucial guidance as they negotiate the difficult terrain of maintenance optimization and digital transformation.