Deploying Lightweight AI models for Predictive Maintenance in Industrial IoT environments
DOI:
https://doi.org/10.56472/ICCSAIML25-123Keywords:
Industrial IoT, Predictive Maintenance, Edge AI, Lightweight Models, Deep LearningAbstract
This paper explores the use of optimized deep learning models – such as quantized and pruned networks that can detect anomalies and predict failures in real – time. This reduces reliance on cloud connectivity by enabling on device inference, thereby reducing latency and improving data privacy. The growing adoption of Industrial Internet of Things (IIoT) has created a need for intelligent and scalable solutions to monitor equipment health and ensure operational continuity
Downloads
References
[1] S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Boston, MA, USA: Pearson, 2020.
[2] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, Cambridge, MA, USA: MIT Press, 2016.
[3] M. Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, 3rd ed. Harlow, U.K.: Pearson Education, 2011.
