Data-Driven Predictive Maintenance Using Artificial Intelligence and Machine Learning
DOI:
https://doi.org/10.54518/jaei.4.1.2026.1474Keywords:
Artificial Intelligence, Data-Driven Predictive Maintenance, Industry 5.0, Machine LearningAbstract
Digital transformation in engineering has accelerated the adoption of Data-Driven Predictive Maintenance (PdM) as a maintenance strategy that utilizes operational data to predict equipment failures more accurately. This study aims to examine recent advances in the application of Artificial Intelligence (AI) and Machine Learning (ML) for predictive maintenance through a Systematic Literature Review (SLR). The review analyzes peer-reviewed studies published over the last five years collected from major scientific databases. The analysis focuses on AI and ML algorithms, data sources, model evaluation techniques, implementation benefits, current challenges, and future research directions. The findings indicate that algorithms such as Random Forest, Support Vector Machine, Extreme Gradient Boosting, Convolutional Neural Network, Long Short-Term Memory, and Transformer have demonstrated strong performance in anomaly detection, fault diagnosis, and Remaining Useful Life prediction. While predictive maintenance significantly improves equipment reliability and maintenance efficiency, several challenges remain, including data quality, model interpretability, cybersecurity, and system integration. The study concludes that the integration of AI, ML, Digital Twin, Edge AI, and Explainable Artificial Intelligence offers promising opportunities to enhance prediction accuracy and support the development of adaptive, intelligent, and sustainable maintenance systems in the industry 5.0 era.
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