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Open Access

ARTICLE

An IoT-Based Predictive Maintenance Framework Using a Hybrid Deep Learning Model for Smart Industrial Systems

Atheer Aleran1, Hanan Almukhalfi1, Ayman Noor1, Reyadh Alluhaibi2, Abdulrahman Hafez3, Talal H. Noor1,*
1 Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, 42353, Saudi Arabia
2 Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, Taibah University, Madinah, 42353, Saudi Arabia
3 Computer Science and Computer Information Technology Department, College of Business, Technology & Professional Studies, Methodist University, Fayetteville, NC 28311, USA
* Corresponding Author: Talal H. Noor. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.070741

Received 23 July 2025; Accepted 04 November 2025; Published online 10 December 2025

Abstract

Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs. Conventional maintenance methods, such as reactive maintenance (i.e., run to failure) or time-based preventive maintenance (i.e., scheduled servicing), prove ineffective for complex systems with many Internet of Things (IoT) devices and sensors because they fall short in detecting faults at early stages when it is most crucial. This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNNs). The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories, including , , and . The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification. The FD004 subset of the NASA C-MAPSS dataset, containing multivariate sensor readings from aircraft engines, serves as the training and evaluation data for the model. Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN, achieving an overall average accuracy of 86.66%, precision of 86.00%, recall of 86.33%, and F1-score of 86.33%. Contrary to the previous LSTM-CNN–based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data, our paper provides a three-class maintenance state (i.e., , , and ) along with threshold-based labeling that retains the true nature of the degradation. In addition, our work also provides an IoT-to-cloud-based modular architecture for deployment. It offers Computerized Maintenance Management System (CMMS) integration, making our proposed solution not only technically sound but also practical and innovative. The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.

Keywords

Predictive maintenance; Internet of Things (IoT); smart industrial systems; LSTM-CNN hybrid model; deep learning; remaining useful life (RUL); industrial fault diagnosis

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