# Pattern Discovery in White Etching Crack Experimental Data Using Machine Learning Techniques
## 1 Introduction  
White etching cracks (WECs) are a phenomenon in industrial gearboxes as well as automotive applications leading to a yet unpredictable failure called white structure flaking (WSF) or white etching cracks [1; 2]. Such failures are responsible for significant operating and downtime costs, especially for wind turbine gearboxes [3; 4; 5]. To put the importance of WEC failures into perspective, WECs can lead to bearing failures within 5%-20% of the bearing's L10 lifespan (DIN ISO 281), leading to unexpected maintenance costs [6; 7; 8; 9; 10; 11]. Several recent investigations aimed at understanding the causes behind WEC related bearing failure in wind turbine gearboxes [5; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. However, the causes of WEC bearing failures remain a subject of debate [5; 10; 21; 22; 23]. Several root cause investigations of WEC failures found that lubricants and their components, e.g., additives, can play an important role in leading to WECs [22; 24; 25]. In one investigation, an experimentwas performed with a lubricant composed of only a base oil (no additives), resulting in no WEC failure even after 1000 h of testing, while another experiment with a lubricant containing over-based calcium sulfonates as rust preventers and short-chain zinc dithiophosphates as antiwear additives resulted in WEC failure after 40 h of testing [22]. [PERSON] et al. also performed life tests on cylindrical roller thrust bearings in different oils [26] A so-called 'WEC critical oil' with additives resulted in premature bearing failure within 5% of the lifetime of another bearing that was lubricated with a mineral oil containing no additives [26]. These results not only implicate the so-called 'WEC critical oil', but also indicate that oil additives may have an influence on risk of WECs. Similarly, several other investigations used a specific oil, containing additives, to successfully promote WEC failure [1; 10; 21; 23; 27], the most recent of which is the investigation by [PERSON] et al., where lubricant additives were systematically varied to study the effect of different additive combinations on bearing time until failure [24]. The investigation found that the lubricant containing zinc dialkyl-dithiophosphate (ZnDDP) led to WECs sooner than any other tested lubricant under the test conditions [24]. 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 could be addressed using machine learning (ML) algorithms that are able to discover patterns in high-dimensional data belonging to several experiments. However, ML algorithms are often criticized for a lack of transparency. Transparency into the drivers of accuracy of ML algorithms are crucial if such algorithms are to be used to identify root causes from experimental data. This paper addresses these issues by first developing machine learning models that are able to learn patterns from experimental data and demonstrate high skill in identifying risky variable combinations from different experiments. The developed models are then further tested following a technique designed to reveal the inner-workings of the models driving the accuracy of their judgements. More specifically, the models were tested to identify which variables are important for the performance of the models and to what extent, relative to one another. In order to train and assess the models in identifying risky conditions with respect to WECs from previous experiments, a dataset containing roughly 700 high- and low-reference oil compositions was used. The data was provided by Schaeffler on the condition that the identities of the constituting oil compounds remained anonymized. The dataset was compiled based on physical tests and chemical simulations performed by Schaeffler in collaboration with 4 LinesFusion, a supplier of industrial analytics solutions [28; 29].