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López Vargas, Wilson
Machine Learning Algorithm Selection for Predictive Maintenance in the Oil Industry
Virtual Reality-Based Immersive Approach for Teaching Basic Electronics Concepts
2024
,
Alexander Briceño-Ruiz
,
López Vargas, Wilson
,
Jahel Riofrío-Vera
,
Steven Paredes-Medina
,
Lourdes Mejía-Ibarra
,
Jose E. Naranjo
One of the applications of artificial intelligence (AI) within the industry is machine learning (ML), a powerful tool to perform predictive maintenance on machinery, as it allows for predicting future failure scenarios through data analysis and avoiding accidents. Electric submersible pumps (ESPs) are critical in the oil industry, as they are used in the artificial lift technique for oil extraction, standing out for their extensive production volume at high depths. However, ESPs are prone to frequent failures. For this reason, this study aimed to compare the performance of three popular nondeterministic ML algorithms in Rstudio, Random Forest (RF), Extreme Gradient Boosting (XGB), and Adaptive Boosting (AB), to determine the best predictive maintenance strategies for an ESP. This paper uses an artificial and public domain dataset containing cycle reading information, machine operating variables such as voltage and pressure expressed in average and standard deviation, maintenance cycle information, and component errors. The results showed that when predicting machine failures with the test dataset, the RF algorithm presented a lower variance (2.31273e-8) and higher average accuracy (99.878%); XGB (variance 4.61843e-8 and average accuracy of 99.850%), and AB (variance 7.688043e-8 and average accuracy of 99.844%). An independent sample Welch’s t-test was performed, and a significant difference was found between the accuracy of the RF and that of the other algorithms. It is concluded that RF proved more suitable for predictive maintenance of an ESP with the dataset used.
2024
,
Michelle-M. Balladares
,
López Vargas, Wilson
,
Jerami-A. Zamora
,
Maicol-S. Garcia
,
Cristian-X. Espin
,
Jose-E. Naranjo
Virtual reality (VR) is a technological tool with great potential to allow users to live exceptional experiences through an immersive environment. In terms of education, VR is an innovative tool that aims to improve teaching-learning methodologies through playful dynamics, which generates greater concentration and interest in learning in students. Its applications range from medical education to engineering, allowing it to simulate and practice in various environments without putting the physical integrity of the users or the devices being used at risk. In this study, an evaluation and comparison of two methods of teaching-learning basic electrical circuits are presented, the first is conventional training, and the second is VR training. For this, two groups have been considered for the sample, a control and an experimental one, to validate the efficiency of the developed system. A knowledge test was applied at the end of each training, where it was observed that the experimental group participants obtained 81.25% of correct questions. In comparison, the control group obtained 60.42%. Finally, the system’s usability was evaluated through the System Usability Scale (SUS) tool, obtaining an average of 75.4, which indicates that although there are several aspects to improve, the system is suitable for teaching basic electronics concepts.