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Enhancing Industrial Processes Through Augmented Reality: A Scoping Review
(2025)
Miranda, Alba
;
Aracely M. Vallejo
;
Paulina Ayala
;
Marcelo V. Garcia
;
Jose E. Naranjo
Augmented reality (AR) in industry improves training and technical assistance by overlaying digital information on real environments, facilitating the visualisation and understanding of complex processes. It also enables more effective remote collaboration, optimising problem solving and decision making in real time. This paper proposes a scoping review, using PRISMA guidelines, on the optimisation of industrial processes through the application of AR. The objectives of this study included characterising successful implementations of AR in various industrial processes, comparing different hardware, graphics engines, associated costs, and determining the percentage of optimisation achieved through AR. The databases included were Scopus, SpringerLink, IEEExplore, and MDPI. Eligibility criteria were defined as English-language articles published between 2019 and 2024 that provide significant contributions to AR applications in engineering. The Cochrane method was used to assess bias. The rigorous selection process resulted in the inclusion of 38 articles. Key findings indicate that AR reduces errors and execution times, improves efficiency and productivity, and optimises training and maintenance processes, leading to cost savings and quality improvement. Unity 3D is the most widely used graphics engine for AR applications. The main applications of AR are in maintenance, assembly, training and inspection, with maintenance being the most researched area. Challenges include the learning curve, high initial costs, and hardware limitations.
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Machine Learning Algorithm Selection for Predictive Maintenance in the Oil Industry
(2024)
Alexander Briceño-Ruiz
;
López Vargas, Wilson
;
Jahel Riofrío-Vera
;
Steven Paredes-Medina
;
Lourdes Mejía-Ibarra
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.
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