This article examines predictive maintenance (PdM) systems for predicting equipment conditionusing artificial intelligence (AI) in industrial plants. The study utilizes theoretical analysis, a comparative studyof international practices, and methods for evaluating the effectiveness of machine learning, deep learning, andsignal processing algorithms in predicting equipment failures. The results demonstrate that AI-based PdM systemscan significantly reduce unexpected downtime, optimize maintenance costs, and extend equipment servicelife. 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.
| Mualliflar | Rixsiboyev , Nozimbek |
|---|---|
| Jurnal | Innovation science and technologiy |
| Nashr sanasi | 2025-06-25 |
| Jild | 1 |
| Son | 6 |
| Til | Ingliz |
| DOI | 10.5281/zenodo.20559282 |
DOI: 10.5281/zenodo.20559282 · Maqolaning asl sahifasi
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.
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