eISSN: 3087-4068 editor@aimjournals.com
All Journals
Submit your article

International Research Journal of Advanced Engineering and Technology

Open Access Peer Review International
Open Access

Intelligent Hybrid AI–IoT Model for Sustainable Infrastructure Maintenance: Predictive Failure Detection and Energy Loss Reduction

DOI https://doi.org/10.55640/irjaet-v03i10-02
PDF
Kasun Perera 1 , Nadeesha Fernando 1 ,
4 Department of Engineering and Technology, Fictional Sri Lanka Institute of Technology, Colombo, Sri Lanka
4 Department of Engineering and Technology, Fictional Sri Lanka Institute of Technology, Colombo, Sri Lanka

Abstract

Critical infrastructure systems increasingly depend on interconnected mechanical, electrical, and cyber–physical assets whose failures can generate substantial operational disruption, energy losses, and maintenance costs. Conventional preventive maintenance approaches, based primarily on fixed schedules and historical inspection, are insufficient for dynamically changing infrastructure conditions. This paper develops an intelligent hybrid Artificial Intelligence–Internet of Things (AI–IoT) model for sustainable infrastructure maintenance by integrating distributed sensing, edge–cloud computing, machine-learning-based anomaly detection, predictive failure assessment, and energy-performance monitoring. The proposed conceptual methodology combines vibration and operational sensing with hybrid analytical models to identify degradation patterns before critical failures occur. Edge processing is incorporated to reduce latency and unnecessary data transmission, while cloud-level analytics support longitudinal asset assessment and model refinement. Explainable AI is further integrated to improve maintenance decisions by making predictive outputs interpretable to infrastructure operators. The synthesized findings indicate that the strongest maintenance architecture is not based on a single predictive algorithm but on coordinated sensing, adaptive analytics, reliability evaluation, and energy-aware decision-making. The model consequently provides a framework for shifting infrastructure maintenance from reactive intervention toward condition-based and sustainability-oriented management. Its principal contribution is the integration of failure prediction and energy-loss reduction within a unified AI–IoT maintenance architecture.

Keywords

Artificial Intelligence, Internet of Things, Predictive Maintenance

References

Ahmed, F., Malik, S., & Kim, J. H. (2023). Deep learning–enhanced vibration analysis for predictive maintenance in industrial rotating machinery. IEEE Access, 11, 55712–55725. https://doi.org/10.1109/ACCESS.2023.3271154
Chen, Y., Li, X., & Zhou, T. (2023). Hybrid machine learning models for real-time anomaly detection in smart grid infrastructure. Energy and AI, 9, 100187. https://doi.org/10.1016/j.egyai.2022.100187
Gao, L., Sun, R., & Zhang, M. (2023). Digital twins and predictive analytics for resilient critical infrastructure: A comprehensive review. Engineering Structures, 282, 115776. https://doi.org/10.1016/j.engstruct.2023.115776
Khalid, A., & Lee, S. (2023). Edge–cloud intelligence for smart industrial systems: A latency-aware and energy-efficient framework. Journal of Industrial Information Integration, 30, 100395. https://doi.org/10.1016/j.jii.2022.100395
Mishra, R., & Kumar, D. (2024). Explainable AI for sustainable predictive maintenance in cyber–physical systems. Computers in Industry, 154, 104006. https://doi.org/10.1016/j.compind.2023.104006
Nanda, P., Srinivasan, B., & Qureshi, M. (2024). Reliability testing frameworks for industrial IoT networks under real-world constraints. Internet of Things, 22, 100718. https://doi.org/10.1016/j.iot.2023.100718
Pourbabaee, B., Kordestani, M., & Adams, G. G. (2022). Fault simulation and deep autoencoder modeling for early detection of bearing failures. Mechanical Systems and Signal Processing, 178, 109313. https://doi.org/10.1016/j.ymssp.2022.109313
Rahman, H., Zhao, X., & Tan, Y. (2024). AI-integrated IoT frameworks for monitoring and optimizing energy performance in industrial operations. Renewable and Sustainable Energy Reviews, 184, 113616. https://doi.org/10.1016/j.rser.2023.113616
Singh, P., Verma, A., & Patel, N. (2022). Adaptive edge computing for real-time analytics in Industry 4.0. Journal of Network and Computer Applications, 204, 103409. https://doi.org/10.1016/j.jnca.2022.103409
Zhao, H., Li, Z., & Feng, Q. (2023). Multi-layer hybrid IoT architecture for sustainable smart manufacturing systems. Sensors, 23(4), 1891. https://doi.org/10.3390/s23041891

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 > >> 

Similar Articles

11-20 of 31

You may also start an advanced similarity search for this article.