Version 1
: Received: 9 September 2024 / Approved: 10 September 2024 / Online: 10 September 2024 (09:01:03 CEST)
How to cite:
Rajakannu, A.; K.P, R.; K, V. Application of Artificial Intelligence in Condition Monitoring for Oil and Gas Industries. Preprints2024, 2024090752. https://doi.org/10.20944/preprints202409.0752.v1
Rajakannu, A.; K.P, R.; K, V. Application of Artificial Intelligence in Condition Monitoring for Oil and Gas Industries. Preprints 2024, 2024090752. https://doi.org/10.20944/preprints202409.0752.v1
Rajakannu, A.; K.P, R.; K, V. Application of Artificial Intelligence in Condition Monitoring for Oil and Gas Industries. Preprints2024, 2024090752. https://doi.org/10.20944/preprints202409.0752.v1
APA Style
Rajakannu, A., K.P, R., & K, V. (2024). Application of Artificial Intelligence in Condition Monitoring for Oil and Gas Industries. Preprints. https://doi.org/10.20944/preprints202409.0752.v1
Chicago/Turabian Style
Rajakannu, A., Ramachandran K.P and Vijayalakshmi K. 2024 "Application of Artificial Intelligence in Condition Monitoring for Oil and Gas Industries" Preprints. https://doi.org/10.20944/preprints202409.0752.v1
Abstract
There are considerable benefits in applying Artificial Intelligence to automation in modern industries including Oil and Gas Industries. Oil and Gas Industries have many complex systems in their production and flow line. The failure of gas equipment will have serious consequences including financial and environmental issues. Condition monitoring of oil and gas equipment provides the condition of components, machines, systems, equipment, data hardware, and even software. The predictive maintenance and industry 4.0 applications have more advantages in petrochemical industries to make a safer economic environment and this paper addresses the Artificial Intelligence (AI) application of condition monitoring for Oil and Gas Industries. To understand the effectiveness of AI in condition monitoring for oil and gas industries, a case study related to the condition monitoring of drilling machine is conducted and applied Artificial Neural Network (ANN) algorithm to analyze and predict the potential failures. The novelty of this work is the proposal of an approach for tool wear monitoring in drilling using acoustic emission sensors for feature extraction and considering wavelet packet decomposition for further analysis. The extracted features from WPD are given as input for ANN to identify the healthiness of the drill bit and machine. This work aims to find the effectiveness of AI-based condition monitoring in enhancing effectiveness of monitoring and the safety of equipment in Oil and Gas Industries.
Keywords
Artificial Intelligence; oil and gas sector; condition monitoring; Artificial Neural Network (ANN); Wavelet Packet Decomposition (WPD)
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
