AI-Enhanced Predictive Maintenance for Industrial Machinery Using IoT Data
Abstract
Predictive maintenance is critical for reducing downtime and operational costs in industrial settings. This paper presents an AI-enhanced framework that leverages IoT sensor data for real-time monitoring and fault prediction in industrial machinery. The system integrates deep learning models with time-series analysis to detect anomalies and predict equipment failures before they occur. Evaluations on industrial datasets show significant improvements in maintenance scheduling and resource optimization. The findings highlight the potential of AI-driven predictive maintenance to enhance efficiency, minimize disruptions, and extend the lifespan of industrial equipment.
References
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778).
Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527-1554.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations (ICLR) (pp. 1-15).
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations (ICLR).
Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.
Singh, S. K., Choudhary, S. K., Ranjan, P., Cognizant, N. J., & Dahiya, S. (2022) COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS AND DATA ANALYTICS TECHNIQUES FOR FRAUD DETECTION IN BANKING SYSTEM.
Ranjan, P., Khunger, A., Satya, C. B. V. V., & Dahiya, S. (2022) Threat Modeling and Risk Assessment of APIs in Fintech Applications.
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2022). Advanced Business Analytics Using Machine Learning and Cloud-Based Data Integration. International Scientific Journal for Research, 4(4).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Leveraging Cloud Computing for Efficient Data Processing in SAP Enterprise Solutions. International Journal of Machine Learning for Sustainable Development, 3(4).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Machine Learning in SAP Workflows: A Study of Predictive Analytics and Automation. Transactions on Latest Trends in Artificial Intelligence, 2(2).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Machine Learning Models for Optimizing SAP-Based Data Processing in Cloud Environments. International Journal of Sustainable Development in Computing Science, 3(3).
Ranjan, P., & Dahiya, S. (2021). Advanced threat detection in api security: Leveraging machine learning algorithms. International Journal of Communication Networks and Information Security, 13(1).
Dhaiya, S., Pandey, B. K., Adusumilli, S. B. K., & Avacharmal, R. (2021) Optimizing API Security in FinTech Through Genetic Algorithm based Machine Learning Model.