A Review on Equipment Health Monitoring Using Machine Learning Techniques

DOI

10.2991/978-94-6463-136-4_32How to use a DOI?

Keywords

Failure prediction; machine learning; production; Industry 4.0; and unexpected downtime

Abstract

Numerous scientific domains have been impacted by current developments in ML, AI and the industrial IOT. It has generated a sea of opportunities for embedding sensors that can be tracked and utilized to gather data practically anywhere. Every area of business, particularly smart manufacturing technology since it began to embrace the Internet of Things, has been highlighted by machine learning models. Instead of adhering to a regular timetable, predictive and preventive procedures are being used to better care for the machine. Within the parameters of this study, we may concentrate on the critical procedures of machine or component failure prediction in the smart industry. The most recent advancement in solutions built on machine learning is also pre sented. This can be accomplished by monitoring the machine on the assembly line and installing various sensors so that data can be obtained from those sensors and properly formatted before being utilized to train the machine using supervised machine learning model. Additionally, the historical data on machine failure can be utilized to forewarn about impending machine failure or breakdown in order to stop the entire production or assembly line from shutting down. Additionally, the obtained data can be used with the ML outlier identification technique.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Pankaj V. Baviskar
AU  - Chitresh Nayak
PY  - 2023
DA  - 2023/05/01
TI  - A Review on Equipment Health Monitoring Using Machine Learning Techniques
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 382
EP  - 396
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_32
DO  - 10.2991/978-94-6463-136-4_32
ID  - Baviskar2023
ER  -