Predictive Maintenance in Industry 4.0 Using Machine Learning Techniques

  • Authors

    • B. Sithik Shah Research scholar, Department of IT, PSG College of Arts and Science, Coimbatore, Tamil Nadu, India. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2019PI0M5Q

    Published 10-04-2019

  • Predictive Maintenance, Industry 4.0, Machine Learning, Industrial Internet of Things, Remaining Useful Life, Condition Monitoring, Deep Learning, Smart Manufacturing

    Issue

    Vol. 2 No. 2 (2019)

    Section

    Articles

    How to Cite

    [1]
    S. Shah, “Predictive Maintenance in Industry 4.0 Using Machine Learning Techniques”, IJADSMC, vol. 2, no. 2, pp. 01–13, Oct. 2019, doi: 10.67228/30713498/IJADSMC-2019PI0M5Q.

  • Abstract

    Predictive Maintenance (PdM) is a key component of Industry 4.0, enabling intelligent and data-driven management of industrial assets. Traditional maintenance strategies are no longer sufficient for complex cyber-physical systems, where reliability and efficiency are critical. With the rise of Industrial IoT (IIoT), large volumes of data can be analyzed using machine learning (ML) techniques to predict equipment failures, estimate remaining useful life (RUL), and optimize maintenance schedules. This paper provides a comprehensive study of ML-based PdM frameworks, covering data-driven, physics-based, and hybrid approaches, including supervised, unsupervised, and deep learning models. A structured methodology is proposed, involving data acquisition, preprocessing, feature engineering, model development, and deployment. Key aspects such as degradation modeling, anomaly detection, and performance evaluation are discussed. Challenges including data imbalance, interpretability, scalability, cybersecurity, and real-time implementation are also analyzed. The paper concludes with future directions such as explainable AI, digital twins, federated learning, and autonomous maintenance systems, offering valuable insights for developing efficient and scalable PdM solutions.

  • References

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