###### Abstract  
The highest costs due to premature failures in wind turbine drivetrains are related to defects in the gearbox, with bearing failures being overrepresented. Vibration monitoring has been identified as the primary tool to detect and diagnose these types of failures. However, late or no signs of the failures are still being reported. Artificial neural networks (ANNs) has been shown to favourably be used as a classifier of bearing failures to increase the detection and diagnosis performance, which requires labelled data when training for all types of considered failures. However, less work has been done with an ANN used to create descriptive functions of the vibration and turbine operation data relationship and thereby negating inherent variance in the vibration data and increasing the detectability when a defect appears. Therefore, this study utilizes the relationship between the rotational speed recorded during a vibration measurement and the calculated condition indicator values of specific bearing failures in three wind turbine gearbox failures. An ANN establishes a function between the rotational speed and condition indicator values with healthy training data collected before the failure occurred. Thereafter, whole datasets leading up to the changing of the gearboxes is used to predict the condition indicator values without the failure influence. The difference between the predicted and true values show an increased sensitivity of the detection in two cases of gearbox output shaft bearing failures as well as indicating a planet bearing failure which with the previous data had gone undetected. vibration measurements; bearing failure; wind turbine drivetrain bearings; artificial neural networks +
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The vibration monitoring is normally done by trend values calculated from consecutive measurements, as well as specific peaks in the frequency domain correlated to different bearing components experiencing defects or gear contacts. Base values of these indicators first need to be established before deviations in new data points from this trend can indicate a defect being present. This procedure is often done by experts needing to make decisions in small ocular differences in the data, based on experience. Thereby, an as sensitive monitoring and signal analysis solution as possible is preferable to increase the certainty of maintenance decisions. Lately, much research has been done with machine learning techniques to more efficiently detect, diagnose and predict bearing failures. Most often, techniques such as artificial neural networks (ANNs) [5; 6; 7; 8; 9], is used as it can design a descriptive function from complex observations [10], or support vector machines (SVMs) [11; 12; 13], used for its high generalization ability [14], are implemented as classifiers if data points can be considered healthy or a fault is present, and in extension which bearing component has a defect. Moreover, other techniques such as the random forest classification technique have been implemented in rolling bearing failure detection and diagnosis to attempt to fully replace the need for an expert analysis of the data and to better handle a large number of input variables [15; 16]. However, such techniques and the multitude of extensions to the base neural networks and SVM techniques introduce their own set of complexities in generalizing an implementation to perform on each individual case and individual machine. Therefore, value can be gained by introducing a machine learning implementation as a step in the current condition monitoring methodology to increase the interpretable signs of deteriorating bearings, which the more complex and advanced techniques are not designed for. Additionally, labelled data, where the presence of a defect cannot be argued, are often needed for training the algorithms, which outside of a simulated or lab environment is difficult to acquire and had up to 2015 largely been overlooked [17].