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October 1, 2025NEWPORT INTERNATIONAL JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCESOpen Access

Artificial Intelligence in Predictive Maintenance for Industry

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Authors

AEAndrew Emmanuel

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Overview

Analysis reveals AI significantly improves maintenance schedules in industrial settings, suggesting enhanced efficiency.

Key Points

  • AI integration in predictive maintenance reduces operational costs and enhances safety in industries like manufacturing and ICT.
  • Machine learning and deep learning techniques are utilized for effective data acquisition, preprocessing, and model development.
  • Challenges include data quality and model accuracy, which impact the overall effectiveness of predictive maintenance frameworks.
  • AI facilitates data-driven practices for maintenance, marking a critical advancement in Industry 4.0, but requires organizational maturity.

Cite This Study

Andrew Emmanuel (2025) studied this question.

synapsesocial.com/papers/68dd91c7fe798ba2fc49857ehttps://doi.org/10.59298/nijses/2025/63.713
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1AI for Predictive Maintenance in Industries2024 · 8 citations
  2. 2AI for Predictive Maintenance in Smart Manufacturing2025 · 8 citations
  3. 3AI-powered predictive maintenance for industrial machinery: A comprehensive analysis of machine learning applications and industrial implementation2022 · 1 citations
  4. 4Predictive Maintenance in Industrial Automation and Smart Manufacturing Applications using Artificial Intelligence2023 · 2 citations
  5. 5AI-driven predictive maintenance for industry 4.0: a systematic review of models, methods, and challenges2026 · 8 citations