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Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 5 (2026): Volume 10, Issue 5, May 2026 | Pages: 328-333
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 16-05-2026
Predictive maintenance plays a critical role in modern industrial environments by minimizing downtime and improving operational efficiency. Traditional maintenance strategies fail to effectively utilize real-time data generated by Industrial Internet of Things (IIoT) systems. This paper proposes a hybrid predictive maintenance framework that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and ARIMA-based forecasting for accurate fault prediction and Remaining Useful Life (RUL) estimation. The proposed model leverages CNN for spatial feature extraction and LSTM for capturing temporal dependencies in multivariate sensor data. The system is evaluated using the NASA C-MAPSS dataset. Experimental results demonstrate that the proposed CNN-LSTM model achieves an accuracy of 96.1%, outperforming traditional machine learning approaches. The framework enables real-time monitoring and improves prediction reliability.
Predictive Maintenance, Industrial Internet of Things (IIoT), Deep Learning, LSTM, CNN-LSTM, RUL.
Monali Patil, Neha Rajput, Chaitali Pardeshi, Gauri Mhaske, Poonam Fegade, & Pooja Kurne. (2026). Predictive Maintenance in Industrial IoT Using Deep Learning. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(5), 328-333. Article DOI https://doi.org/10.47001/IRJIET/2026.105043
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