Comparison Analysis of ANN and XGBoost Algorithm with WPD in exploring the application of Artificial Intelligence in condition monitoring for oil and gas industries

There are considerable benefits to applying Artificial Intelligence (AI) to automation in modern industries, including oil and gas. Oil and Gas Industries have many complex systems in their production and flow line. Condition monitoring of oil and gas equipment provides the fault diagnosis 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. To understand the effectiveness of AI in condition monitoring for oil and gas industries, a case study related to the condition monitoring of drilling machines is conducted, and an ANN and XG Boost ensemble algorithm is used to analyze and predict potential failures. In the signal collection, acoustic emission sensors, signal conditioning devices, NI-data acquisition cards, and LabVIEW software were used. The dataset was collected using a healthy tool, a worn tool, and a tool with a broken tip. The novelty of this work is the proposal of an approach for tool wear monitoring in drilling using acoustic emission sensors and considering Wavelet Packet Decomposition (WPD) for further analysis for feature extraction. The extracted features from WPD are given as input for the ANN and XG Boost Algorithm for classification to identify the health of the drill bit and machine. The result shows that the prediction accuracy is higher in the XG Boost algorithm than ANN. Average accuracy of 94% is achieved in XG Boost whereas a 90% average accuracy is achieved in ANN. So, a detailed analysis has been made to compare the ANN and XG Boost performance, finding the effectiveness of AI-based condition monitoring in enhancing the monitoring effectiveness and equipment safety in Oil and Gas Industries.