ANAMOLY DETECTION IN INDUSTRIAL MACHINERY USING IOT DEVICES AND ML

Authors

  • Mrs.A.Aruna , T.Durgeshwari , K.Sneha , D.Navya Sri, Y.Sneha

Keywords:

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Abstract

This task presents a clever way to deal with improved modern hardware support and executionobserving through the coordination of IoT gadgets and AI calculations. the system includes a collection ofsensors, such as fire sensors, sound sensors, and vibration sensors, to provide a comprehensive assessmentof the state of industrial motors. The ESP8266 microcontroller ensures seamless connectivity. In real time,deviations from normal operating conditionS

References

Jay Lee, Hung-An Kao, and Shanhu Yang. Service innovation and smart analytics for industry 4.0 and big data environment. Procedia cirp, 16:3–8, 2014.

Lukas Kaupp, Heiko Webert, Kawa Nazemi, Bernhard Humm, and Stephan Simons. Context: An industry 4.0 dataset of contextual faults in a

smart factory. Procedia Computer Science, 180:492–501, 2021

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Published

2024-01-02

How to Cite

Mrs.A.Aruna , T.Durgeshwari , K.Sneha , D.Navya Sri, Y.Sneha. (2024). ANAMOLY DETECTION IN INDUSTRIAL MACHINERY USING IOT DEVICES AND ML . Journal of Computational Analysis and Applications (JoCAAA), 32(1), 829–836. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2691

Issue

Vol. 32 No. 1 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.