In response to global climate change problem, efforts have begun in industry to achieve carbon neutrality by reducing emissions of CO₂, a greenhouse gas. This article explains the movement toward carbon neutrality by steel manufacturers that manufacture the steel that is used as the base material for rolling bearings, as well as trends in research and development of bearing steel materials with an eye toward contributing through use of products. In the steel manufacturing process, research is being conducted into the direct reduction process of iron ore, promotion of higher efficiency in the heating process, and consideration of switching from fossil fuels. To extend the life of bearings, research is underway to reduce the harmful effects of inclusions based on the rolling fatigue mechanism.

Electrification in the automotive industry is a key solution to environmental issues. High-power and compact drive motors are required to extend the driving range of electric vehicles (EVs). The simple miniaturization of motors can lead to a decrease in power, necessitating high-speed rotation for the drive motors of EVs. Coping with this high-speed rotation presents various issues for the rolling bearings that support rotation in the power transmission of EVs, such as retainer deformation due to centrifugal force, temperature rise, and starved lubrication. This paper discusses recent technological trends in high-speed rotation of rolling bearings used in the power transmission of EVs, along with the exploration of measurement and analysis technologies that support these developments.

In order to reduce greenhouse gas emissions, low viscosity and small quantities of filling greases are used in rolling bearings for low toque. This causes lubrication conditions of rolling bearings more severe. Under such conditions, it is necessary to accurately predict the lubrication conditions to maintain the required functions of the bearings. Lubricating grease shows complex flow behavior inside tolling bearings. This makes understanding of grease lubrication difficult. This article introduces some of the research on the phenomena related to grease lubrication to predict the grease lubrication condition inside bearings.

In recent electric vehicles, the voltage of the batteries installed in them is becoming higher. There is concern that high voltages may be applied to rolling bearings used in the electric vehicle drive systems. When stray current flows through the rolling bearing, electric pitting occurs to the rolling surfaces inside the bearing. This article explains the damage that occurs in the rolling bearing in an energized environments and the damage formation process, as well as provide a broad explanation of the corrosion control technologies for the rolling bearing.

In consideration of global warming and energy issues, rolling bearings used in automobile and various machines are increasingly required to comply with severe conditions, in which high speed rotating, high contact pressure, small amount of lubricant, low lubricant viscosity and high concentration hydrogen atmosphere are adopted. Under these conditions, premature failures for hydrogen embrittlement are occurred frequently. In this paper, the mechanism and countermeasures for hydrogen embrittlement of rolling bearings are explained.

Thermohydrodynamic lubrication (THL) models have been applied to predict the performances of journal bearings that support industrial rotating machineries. This article aims at deriving a model expression that can easily predict the maximum bearing temperature in the THL database of cylindrical journal bearing with two-axial oil grooves, by applying deep learning (DL). The expression is given only four dimensionless bearing design variables. The DL model consists of a multi-layer perceptron with a preprocessing layer, four hidden layers, an output layer and a postprocessing layer. All nodes in adjacent layers are fully connected. Logarithmic function is employed as preprocessing function for the bearing design variables and the maximum bearing temperature, and inverse tangent function as activation function. Six nodes are arranged in each hidden layer. The dataset with the maximum bearing temperature above 0.2 is selected for the training data. Learning is continued until the maximum relative errors for the training and the validation data simultaneously fall within the criterion value. The model expression is shown to be capable of easily predicting the maximum bearing temperature quickly and with high generalization performance and is concluded to be quite useful in significantly reducing the time required to THL design of the bearing.