The appearance of White Etching Cracks (WEC), not covered by the ISO 281 modified failure rate calculation, leads to difficulties in predicting bearing reliability. This uncertainty in bearing applications leads to a worldwide activity in order to understand and prevent this situation since the WEC failure mode deviates from the traditional Rolling Contact Fatigue (RCF) mode. Plenty of factors have been found to influence this phenomenon over the years, however the precise initiation of the WEC is still under debate. In order to understand the initiation and analyze the temporal evolution, interrupted tests on the same material were performed under conditions that were known to lead to WEC formation and RCF. To avoid the added complexity of boundary lubrication, a Deep Groove Ball Bearing (DGBB) test rig under full lubrication (Elastohydrodynamic Lubrication, EHL) was chosen. Within a standard operating mode, named Mode 1 (RCF), the bearings are solely subjected to a radial load. By suspending the tests at different time steps, a continuous progress of changes in the subsurface material structure seen as equiaxed grains with low dislocation densities, identified as ferrite, is observed. The bearings did not fail up to load cycles of 10⁹. In contrast, a Mode 2 Electrical Charged Contact Fatigue (ECCF) test provoked the early formation of cracks and crack networks, first without WEA, then later with WEA. It became obvious when comparing Mode 1 (RCF) with Mode 2 (ECCF) that Mode 2 (ECCF) achieves far fewer load cycles until failure occurs.

An investigation on the White Etching Crack (WEC) phenomenon as a severe damage mode in bearing applications led to the observation that in a latent pre-damage state period, visible alterations appear on the surface of the raceway. A detailed inspection of the microstructure underneath the alterations reveals the existence of plenty of nano-sized pores in a depth range of 80 µm to 200 µm. The depth of the maximum Hertzian stress is calculated to be at 127 µm subsurface. The present study investigates the effect of these nanopores on the fatigue crack initiation in SAE 52100 martensitic hardened bearing steel. In this sense, two micro-models by means of the Finite Element Method (FEM) are developed for both a sample with and a sample without pores. The number of cycles required for the crack initiation for both samples is calculated, using the physical-based Tanaka–Mura model. It is shown that pores reduce the number of cycles in bearing application to come to an earlier transition from microstructural short cracks (MSC) to long crack (LC) propagation significantly.

The reliability of rolling bearings is affected by white etching crack (WEC) or white structure flaking (WSF) failures, causing tremendous commercial burdens for bearing manufacturers and operators. The research for the underlying failure mechanism has attracted interest from a large scientific community over decades. Despite the significant amount of efforts, a root cause of white etching cracking is still missing. Amongst other factors, lubricant chemistry is considered to be essential in WEC formation. The authors aim to elucidate this key parameter by provoking white etching crack formation on a FE8 bearing test rig using a well-described set of chemicals in high- and low-reference lubricants. Scanning electron microscopy and energy dispersive X-ray analysis prove the presence of a patchy tribofilm on the surface of bearing washers, leading most likely to a higher frictional torque at the early stages of operation when the low reference oil is used. Secondary neutral mass spectrometry (SNMS) shows a hydrogen containing tribofilm in the shallow subsurface of about 30 nm depth, suggesting that hydrogen proliferating into bearing material may subsequently facilitate crack propagation via dislocation pileups, leading to premature bearing failure.

The problem of White Etching Cracks (WEC) leading to failures in the life cycle of industrial and automotive drive trains has existed for more than 30 years. Many parameters leading to WEC, such as the presence of electricity, unfavorable lubricants, and other factors, have been identified by the use of test rigs. However, since (a) within lifecycle operation these impacts appear incidentally, and (b) the identified impacts stimulate each other as stated in WEC research, the appearance of WEC in a drive train is impossible to predict for any given application. This difficulty causes WEC to be a serious problem. Obtaining reliable failure prediction rates using construction guidelines is still impossible, resulting in warranty claims. The aim of this paper is, first, to establish a routine to determine if and how lubricants could be described numerically with respect to their susceptibility toward electrical fields and to see how this factor could be added in a later stage to construction guidelines. Second, this paper aims to give advice to the applicant in terms of countermeasures. Currently, as predictors are missing, extensive and time-consuming testing is required. Contradictory test results in the field of application, indicate the need for fundamental parameters to determine in which circumstances the application is exposed to WEC risk and to suggest countermeasures. The current study presents a simulation method based solely on the chemical structure of lubricant components, investigating the appearance of WEC and their response to increasing electrical fields. The results show a clear pattern in WEC criticality with respect to lubricants: if two or more components present in the lubricant create clusters through their dipolar interaction, an apparent WEC risk could be accurately predicted apart from test rig results. These clusters are charged like one big particle in an early stage of electrical field exposure. As a result, the surface area increases, facilitating a higher uptake of charge. The incidental breakdown of this charge is assumed. As the charge of a cluster is higher than that of single molecules, WEC critical lubricants are subjected to this pattern. The study validates these results by comparing them using lubricants known to be critical with respect to WEC, suggesting further tests in the near future. Despite the fact that some lubricants seem more critical than others related to WEC, each lubricant clearly might lead to WEC under given conditions. Thus, constructing guidelines for a prediction routine is essential.

In bearing applications, the presence of stray and parasitic currents in combination with lubricants has been studied for almost a century and has been found to cause fluting and corrugation damages under high current densities. However, recent research has suggested that at low current densities (<1 mA/mm²) under specific operating conditions, electrical discharges can substantially reduce bearing life due to the formation of white etching cracks (WECs). To date, limited studies have investigated the critical operating and electrical conditions for WEC formation and demonstrated effective fault detection techniques. This study uses a novel monitoring technique known as the electrostatic sensing technique to detect, monitor and characterise electrical discharges in an oil-lubricated steel–steel rolling contact on a TE74 twin-roller machine. The findings demonstrate that WECs can be formed under the influence of electrical discharges in less than 50 h, and the electrostatic sensors are effective for the early detection of critical electrical discharges related to WEC-induced failures.

White etching crack (WEC) failure is a failure mode that affects bearings in many applications, including wind turbine gearboxes, where it results in high, unplanned maintenance costs. WEC failure is unpredictable as of now, and its root causes are not yet fully understood. While WECs were produced under controlled conditions in several investigations in the past, converging the findings from the different combinations of factors that led to WECs in different experiments remains a challenge. This challenge is tackled in this paper using machine learning (ML) models that are capable of capturing patterns in high-dimensional data belonging to several experiments in order to identify influential variables to the risk of WECs. Three different ML models were designed and applied to a dataset containing roughly 700 high- and low-risk oil compositions to identify the constituting chemical compounds that make a given oil composition high-risk with respect to WECs. This includes the first application of a purpose-built neural network-based feature selection method. Out of 21 compounds, eight were identified as influential by models based on random forest and artificial neural networks. Association rules were also mined from the data to investigate the relationship between compound combinations and WEC risk, leading to results supporting those of previous analyses. In addition, the identified compound with the highest influence was proved in a separate investigation involving physical tests to be of high WEC risk. The presented methods can be applied to other experimental data where a high number of measured variables potentially influence a certain outcome and where there is a need to identify variables with the highest influence.