Predictive maintenance is a practice that industrial companies can apply to their processes thanks to technologies such as artificial intelligence and the Internet of Things. Machine Learning algorithms are used in many fields to make predictions or classifications. Predictive maintenance is an area of research that provides new practices, strategies, or methodologies. As a relatively new field, the methodologies are still scattered and there is little information on the maturity of the algorithms. To provide a solid foundation, a systematic literature review is presented to give engineers and researchers an overview of machine learning algorithms used in predictive maintenance. The results obtained show some growth in recent years, demonstrating the interest in this area of research. However, most of the contributions in this field can be summarized as concept proofs and it is still difficult to obtain a prototype that can be validated as a complete and certified system. This paper describes the main machine learning algorithms used in predictive maintenance according to their type of use and supervision, analyses their input and output parameters, and determines their maturity.

The advent of Industry 4.0 has precipitated the digitization of myriad industrial processes, a feat attributable to the implementation of sophisticated digital enablers such as artificial intelligence (AI) and the Internet of Things (IoT). These technological advances have facilitated the implementation of various innovative applications, especially in the field of predictive maintenance. This approach facilitates more precise estimation of the remaining useful life (RUL) of equipment, determination of the health index (HI) of machinery, and planning of effective maintenance schedules that circumvent unexpected and costly shutdowns in industrial operations. The employment of hybrid approaches founded on machine learning algorithms in the domain of predictive maintenance signifies a perpetually evolving field of research, wherein novel techniques, methodologies, and strategies are proposed to enhance maintenance efficiency and reliability. In order to furnish a substantial and exhaustive compendium of information, a methodical literature review is hereby presented, offering a meticulous survey of the hybrid approaches utilized within this domain. The study analyzed 77 papers from the 914 papers found on the topic, to find and organize the body of knowledge, and presents a lucid taxonomy, the primary algorithms employed in hybrid approaches, the most prevalent datasets, the applicable technology architectures, and the maturity level of these solutions. This study provides a robust conceptual foundation for future research, underscoring the significance of hybrid approaches as a promising field of study, with considerable potential for advancement in the realm of industrial predictive maintenance.

Los molinos verticales de rodillos VRM, son máquinas muy utilizadas para moler materia prima en fábricas dedicadas a la extracción y procesamiento de minerales. Cualquier máquina ocupada para moler o triturar consume alrededor del 30 o 40% de la energía de una fábrica. La presión de carga, la velocidad de rotación de la mesa, el contenido de humedad, temperatura de salida del molino y presión de los rodillos son variables que se pueden controlar para disminuir el consumo de energía especifica Ecs. Este trabajo plantea un problema de optimización con el fin de reducir el consumo energético de un VRM utilizado para producir cemento y encontrar los parámetros óptimos de las variables de operación. Se utilizan varios paquetes para resolver el problema de programación no lineal, obteniendo resultados muy concisos, pero se usa los resultados proporcionados por el paquete Pyomo. Comparando el resultado de la función objetivo con el consumo energético de una empresa cementera en Ecuador se concluye que los parámetros optimizados son capaces de reducir en un 25% el consumo energético garantizando una producción mínima de 2200 toneladas de cemento diarias por lo que el modelo se valida correctamente.

The present article introduces TRANS-D3, an innovative hybrid method that combines the Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning algorithm with the Transformer architecture for predicting the remaining useful life (RUL). The model utilizes an optimized reward function based on the Linear Quadratic Regulator (LQR) to approach error correction as a dynamic control problem. On the CMAPSS dataset, TRANS-D3 demonstrates a marked advantage, achieving RMSE reductions of 84–90% in baseline situations (FD001) and 23–45% in highly variable contexts (FD003/FD004). Statistical validation demonstrates high reliability, with a coefficient of determination $R^2$ of more than 0.93 in each of the subsets; the maximum is 0.9984 in FD001. The 95% confidence intervals for the mean error, ranging from [0.709 to 1.244] in FD001 and from [−1.324 to 1.748] in FD004, also confirm that the framework is a statistically unbiased estimator. In terms of Score, the model reduces penalties by between 80% and 95% compared to advanced architectures such as DAST or STAR, ensuring very stable predictions. These findings present a novel robust optimization paradigm, which is essential for ensuring the safety and reliability of complex industrial systems in the context of Industry 4.0.

Direct flame burners are extensively used in forging processes to heat metal billets. Among these, atmospheric or suction burners are the most prevalent due to their simple construction and low cost. These devices operate based on the Venturi effect, which introduces air into the mixing chamber to allow premixing with the fuel gas before combustion begins. The proper functionality of these devices depends on numerous construction parameters that determine the air-to-gas ratio in the mixture, directly affecting their performance. This study uses computational fluid dynamics (CFD) to compare two atmospheric burner designs. The first geometry corresponds to a conventional design assembled from iron pipe fittings, while the second follows more advanced design principles described in the technical literature. Simulations performed in ANSYS Fluent reveal that the shape of the suction chamber has a strong impact on the location of the low-pressure region and on the direction of airflow toward the liquefied petroleum gas (LGP). A comparison between both configurations shows that the geometry and the setting of the primary air regulator significantly affect burner efficiency by modifying the air entrainment ratio. These findings underscore the importance of geometrical design in enhancing the mixing process and the overall performance of atmospheric burners.