AI-Driven Predictive Maintenance in Industrial IoT

Main Article Content

Shiva Vishnu

Abstract

Predictive maintenance is a critical application of Artificial Intelligence (AI) in the context of Industrial Internet of Things (IIoT). This approach harnesses the power of AI to monitor, analyze, and predict the maintenance needs of machinery and equipment in industrial settings. By proactively identifying issues and scheduling maintenance before breakdowns occur, predictive maintenance helps to improve equipment reliability, reduce downtime, and ultimately save costs.


This paper presents a comprehensive overview of AI-driven predictive maintenance in the context of Industrial IoT. It covers various machine learning algorithms, sensor technologies, and data processing techniques used for predictive maintenance. Furthermore, the paper discusses the challenges and opportunities in implementing AI-driven predictive maintenance systems, including data quality, model accuracy, and real-time decision-making.

Downloads

Download data is not yet available.

Article Details

How to Cite
AI-Driven Predictive Maintenance in Industrial IoT. (2022). Research-Gate Journal, 8(8). https://research-gate.in/index.php/Rgj/article/view/15
Section
Articles

How to Cite

AI-Driven Predictive Maintenance in Industrial IoT. (2022). Research-Gate Journal, 8(8). https://research-gate.in/index.php/Rgj/article/view/15

References

Chaitanya Krishna Suryadevara, “TOWARDS PERSONALIZED HEALTHCARE - AN INTELLIGENT MEDICATION RECOMMENDATION SYSTEM”, IEJRD - International Multidisciplinary Journal, vol. 5, no. 9, p. 16, Dec. 2020.

Suryadevara, Chaitanya Krishna, Predictive Modeling for Student Performance: Harnessing Machine Learning to Forecast Academic Marks (December 22, 2018). International Journal of Research in Engineering and Applied Sciences (IJREAS), Vol. 8 Issue 12, December-2018, Available at SSRN: https://ssrn.com/abstract=4591990

Suryadevara, Chaitanya Krishna, Unveiling Urban Mobility Patterns: A Comprehensive Analysis of Uber (December 21, 2019). International Journal of Engineering, Science and Mathematics, Vol. 8 Issue 12, December 2019, Available at SSRN: https://ssrn.com/abstract=4591998

Chaitanya Krishna Suryadevara. (2019). A NEW WAY OF PREDICTING THE LOAN APPROVAL PROCESS USING ML TECHNIQUES. International Journal of Innovations in Engineering Research and Technology, 6(12), 38–48. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3654

Chaitanya Krishna Suryadevara. (2020). GENERATING FREE IMAGES WITH OPENAI’S GENERATIVE MODELS. International Journal of Innovations in Engineering Research and Technology, 7(3), 49–56. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3653

Chaitanya Krishna Suryadevara. (2020). REAL-TIME FACE MASK DETECTION WITH COMPUTER VISION AND DEEP LEARNING: English. International Journal of Innovations in Engineering Research and Technology, 7(12), 254–259. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3184

Chaitanya Krishna Suryadevara. (2021). ENHANCING SAFETY: FACE MASK DETECTION USING COMPUTER VISION AND DEEP LEARNING. International Journal of Innovations in Engineering Research and Technology, 8(08), 224–229. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3672