This paper focuses on advancements in predictive maintenance driven by artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). It explores applications in the predictive maintenance in industries, aiming to provide a comprehensive understanding of current methodologies and future prospects. The discussion focuses on predictive maintenance methodologies, highlighting strengths, limitations, challenges, and opportunities.

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[1] Gourav Vivek Kulkarni, "IIoT EL Expt Data", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/kg7p-en84. Accessed: Oct. 10, 2024.
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doi = {10.21227/kg7p-en84},
url = {http://dx.doi.org/10.21227/kg7p-en84},
author = {Gourav Vivek Kulkarni },
publisher = {IEEE Dataport},
title = {IIoT EL Expt Data},
year = {2024} }
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T1 - IIoT EL Expt Data
AU - Gourav Vivek Kulkarni
PY - 2024
PB - IEEE Dataport
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Gourav Vivek Kulkarni. (2024). IIoT EL Expt Data. IEEE Dataport. http://dx.doi.org/10.21227/kg7p-en84
Gourav Vivek Kulkarni, 2024. IIoT EL Expt Data. Available at: http://dx.doi.org/10.21227/kg7p-en84.
Gourav Vivek Kulkarni. (2024). "IIoT EL Expt Data." Web.
1. Gourav Vivek Kulkarni. IIoT EL Expt Data [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/kg7p-en84
Gourav Vivek Kulkarni. "IIoT EL Expt Data." doi: 10.21227/kg7p-en84