The recent decline in crude oil prices due to global competition and COVID-19-related demand issues has highlighted the need for the efficient operation of an oil and gas plant. One such avenue is accurate predictions about the remaining useful life (RUL) of components used in oil and gas plants. A tribosystem is comprised of the surfaces in relative motion and the lubricant between them. Lubricant oils play a significant role in keeping any tribosystem such as bearings and gears working smoothly over the lifetime of the oil and gas plant. The lubricant oil needs replenishment from time to time to avoid component breakdown due to the increased presence of wear debris and friction between the sliding surfaces of bearings and gears. Traditionally, this oil change is carried out at pre-determined times. This paper explored the possibilities of employing machine learning to predict early failure behavior in sensor-instrumented tribosystems. Specifically, deep learning and tribological data obtained from sensors deployed on the components can provide more accurate predictions about the RUL of the tribosystem. This automated maintenance can improve the overall efficiency of the component. The present study aimed to develop a deep learning-based digital twin for accurately predicting the RUL of a tribosystem comprised of a ball bearing-like test apparatus, a four-ball tester, and lubricant oil. A commercial lubricant used in the offshore oil and gas components was tested for its extreme pressure performance, and its welding load was measured using a four-ball tester. Three accelerated deterioration tests was carried out on the four-ball tester at a load below the welding load. Based on the wear scar measurements obtained from the experimental tests, the RUL data were used to train a multivariate convolutional neural network (CNN). The training accuracy of the model was above 99%, and the testing accuracy was above 95%. This work involved the model-free learning prediction of the remaining useful lifetime of ball bearing-type contacts as a function of key sensor input data (i.e., load, friction, temperature). This model can be deployed for in-field tribological machine elements to trigger automated maintenance without explicitly measuring the wear phenomenon.

There exists an ever-growing need for sustainable engineering solutions to improve emission control and the energy efficiency of tribosystems. This study examines the tribological properties of two environmentally friendly vegetable fluids, soybean and sunflower oil, with the addition of three non-toxic nanostructures (h-BN, silver and MgO) at different concentrations. It was found that nanostructures added to vegetable oils at specific concentrations can exhibit good friction reduction and wear preventive properties. The addition of h-BN nanosheets in sunflower oil decreased the coefficient of friction and the wear damage, as measured by the wear scar diameter. Silver and magnesium oxide nanoparticles further reduced the friction and wear, respectively. In addition to the tribological testing of the samples, investigations were performed using an optical microscope, SEM and EDX to elucidate the mechanisms that may have led to the observed friction reduction and wear-preventive properties of different nanostructure additives. The thermophysical properties of the samples were also measured. It was found that the thermal conductivity of both base oils could be enhanced by 24% when using h-BN at 0.25 wt% concentration.

Accurately predicting friction and wear in lubricated tribosystems is essential for improving mechanical performance and durability. However, models that simultaneously predict the COF and wear remain scarce due to the complex, non-linear nature of these phenomena and the absence of a direct correlation between them. Tribology has traditionally relied on experimental methods to quantify these properties. In this study, a data-driven machine learning model is developed to predict the COF and WSD over time using data from four-ball tester experiments with lubricants containing various additives. Input parameters include lubricant properties, additive properties, and operating conditions. The data-driven model effectively captures the complex interactions among these variables, demonstrating high predictive accuracy for both the COF and WSD. These findings underscore the potential of deep learning in partnership with large data sets, overcoming modeling limitations in tribology, offering a valuable tool for predictive maintenance and lubricant optimization in industrial applications.

The prediction of tribological behavior in engineering systems based on output data streams is often considered empirical, as it is difficult to fully describe the underlying processes using closed-form mathematical models. For instance, accurately predicting the coefficient of friction (COF) across the three major lubrication regimes using a single model remains elusive. Machine learning (ML) offers a powerful data-driven approach for modeling complex tribological processes by learning nonlinear relationships between operating conditions and performance metrics. This work develops a high-accuracy deep learning model for predicting the COF in lubricated tribosystems across boundary, mixed, and hydrodynamic lubrication regimes. Extensive experimental data were collected using a ball-on-disk tribometer, which generated a large and diverse dataset spanning multiple materials, lubricants, temperatures, loads, and sliding speeds and covered the full Stribeck curve. These data were then used to train and optimize a neural network capable of accurately reproducing frictional transitions across lubrication regimes. The breadth of the dataset not only allows for the full Stribeck behavior to be captured, but also enables generalization across distinct tribosystems. The resulting model demonstrates strong predictive performance and is deployed using a graphical user interface as a practical tool for estimating COF in ball-on-disk lubricated sliding contacts.

Sustainable manufacturing that prioritizes energy efficiency, minimal water use, scalability, and the ability to generate diverse materials is essential to advance inorganic materials production while maintaining environmental consciousness. However, current manufacturing practices are unable to meet these requirements. Here, we describe a new flash-within-flash Joule heating (FWF)—a non-equilibrium, ultrafast heat conduction method—to prepare 13 transition metal dichalcogenides (TMD) and 9 non-TMD materials, each in under 5 seconds while in ambient conditions. FWF achieves enormous advantages in facile gram scalability and in sustainable manufacturing criteria when compared to other synthesis methods. Also, FWF allows the production of phase-selective and single-crystalline bulk powders, a phenomenon rarely observed by any other synthesis method. Furthermore, FWF-made MoSe2 outperformed commercially available MoSe2 in tribology, showcasing the quality of FWF-made materials. The capability for atom substitution and doping further highlights the versatility of FWF as a general bulk inorganic materials synthesis protocol.

Crude oil prices have historically shown volatility that is difficult to forecast. The typical response is for fuel recovery companies to lower the oil extraction costs or increase operational efficiency as much as possible. One such avenue that can help achieve higher operating efficiency for a remote, offshore, and subsea oil and gas plant is to achieve appropriately timed