An Integrated Artificial Intelligence and Big Data Analytics Framework for Accurate Machine Failure Prediction
DOI:
https://doi.org/10.54518/jaei.3.2.2025.1360Keywords:
Artificial Intelligence, Big Data Analytics, Machine Failure Prediction, Predictive Maintenance, Smart ManufacturingAbstract
Machine failure prediction has become a critical component of predictive maintenance in smart manufacturing environments. The growing complexity of industrial systems and the massive volume of data generated by Industrial Internet of Things (IIoT) sensors require intelligent approaches capable of processing large-scale data while providing accurate predictions. This study proposes an integrated conceptual framework that combines Artificial Intelligence (AI) and Big Data Analytics (BDA) to improve machine failure prediction accuracy. A Systematic Literature Review (SLR) was conducted by synthesizing peer-reviewed studies published over the last five years. The findings indicate that Big Data Analytics supports industrial data acquisition, storage, integration, preprocessing, and feature engineering, whereas Artificial Intelligence enables pattern learning, degradation identification, and machine failure prediction through intelligent algorithms. The integration of these technologies establishes a comprehensive predictive maintenance workflow that transforms industrial data into actionable maintenance decisions. The proposed framework is expected to improve prediction accuracy, reduce unplanned downtime, optimize maintenance scheduling, and enhance equipment reliability. Furthermore, it provides a conceptual foundation for future empirical research and practical implementation of intelligent predictive maintenance across various industrial sectors.
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