SANOAT KORXONALARIDA USKUNALAR HOLATINI SUN’IY INTELLEKT ORQALI BASHORATLASH
DOI:
https://doi.org/10.5281/zenodo.20559282Abstract
This article examines predictive maintenance (PdM) systems for predicting equipment condition
using artificial intelligence (AI) in industrial plants. The study utilizes theoretical analysis, a comparative study
of international practices, and methods for evaluating the effectiveness of machine learning, deep learning, and
signal processing algorithms in predicting equipment failures. The results demonstrate that AI-based PdM systems
can significantly reduce unexpected downtime, optimize maintenance costs, and extend equipment service
life. Using international experience as examples, the PdM methods of leading companies, such as Rolls-
Royce, Caterpillar, ThyssenKrupp, and SKF, are analyzed and their results are presented. The key stages,
technical requirements, and cost effectiveness of implementing AI-based PdM systems are substantiated.
Keywords
AI, PdM, predictive maintenance, equipment condition, failure prediction, remaining service life, vibration analysis, anomaly detection, machine learning, deep learning, signal processing, IoT, real-time monitoring, Industry 4.0.References
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