Applications of Artificial Intelligence in predictive maintenance of mechanical equipment: methods, results, and challenges
DOI:
https://doi.org/10.34117/bjdv11n12-015Keywords:
Artificial Intelligence, predictive maintenance, mechanical equipment, machine learning, internet of things, condition monitoring, operational efficiencyAbstract
Artificial intelligence (AI) has revolutionized predictive maintenance for mechanical equipment by enabling the early detection of faults and accurate prediction of failures through advanced machine learning algorithms and real-time sensor data analysis. This paper provides a comprehensive review of AI applications in predictive maintenance, focusing on data acquisition techniques, feature extraction, model development, and real-time monitoring systems. The integration of AI and the Internet of Things (IoT) enables continuous equipment health assessment, significantly reducing unplanned downtime and maintenance costs while extending machinery lifespan. The benefits are evident in improved operational efficiency, cost savings, and enhanced decision-making for maintenance scheduling. Challenges such as data quality, model interpretability, and system integration are discussed, along with future trends including explainable AI and edge computing for more responsive maintenance solutions.
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