###### Abstract
Thereby, an implementation that does not require labelled data has an advantage when applied to existing wind turbine vibration field data. However, studies into various condition monitoring techniques using vibration field data as opposed to simulated or test rig data are starting to become more popular. In [PERSON]'s bearing condition monitoring review from 2016 less than 8% were using field data while in [PERSON]'s review from 2020, this had increased to 40%. A number of machine learning techniques were employed in some of these studies, but only one used an ANN [18; 19]. Work using wind turbine field data has also been focused on using data from the turbine Supervisory Control And Data Acquisition (SCADA) system in ANNs to detect defects [20; 21; 22]. Here, parameters such as the wind speed, blade pitch angle, drivetrain component temperatures and generated power can be used to find abnormal behaviour in the turbines state and operation which could indicate a defect being present. Keeping in mind that among the typical monitoring techniques, vibration monitoring can detect defect at an earlier stage compared to lubrication and temperature monitoring, this would still be used as the main indicator for a defect being present. However, the integration between the SCADA and condition monitoring systems data sources is often non-existent [23]. Therefore, a lot of hands-on work is needed in each individual case to prepare the data which greatly hampers a machine learning solution to be widely introduced in wind turbine monitoring. An implementation that at most only requires the input of operational data which is stored as metadata to the vibration measurements, where the integration of the two data types exists, is therefore of interest. A first step to introduce a machine learning solution can be to make use of the few operational parameters which exist in the condition monitoring system and find abnormal behaviour to correlate to the presence of a defect. Another important advantage to take into consideration with such a solution is the need to obtain labelled data only when healthy for the learning period, instead of when used as a classifier where both healthy data and labelled data during all types of failures are needed. This study utilizes vibration monitoring results leading up to three different wind turbine gearbox bearing failures. More specifically condition indicator values for the specific failures, providing the detection and diagnosis, is used. The three failures consist of two gearbox output shaft bearing failures which are clearly detectable, and one planet bearing failure from the second planetary gear stage which is problematic to detect and diagnose. The novelty of this study is showing the implementation of an ANN regression solution, chosen for its highly adaptable ability to quickly design functions from complex observations, between condition indicator results when the healthy and rotational speed of the gearbox output shaft, an associated measurement to the vibration measurements within the condition monitoring database, enabling an implementation without the need for labelled data. Thereby, the ANN implementation can be used to predict the condition indicator value as it should be during normal operation and when compared to true values with developing defects affecting the system, exhibit this difference as a more sensitive detection method than purely the condition indicator values themselves. The goal of this study is then to improve the sensitivity of the condition monitoring system and highlight signs of a defect that might not be possible by pure ocular observations of the condition indicator trends. In Section 2, the ANN implementation methodology is presented with summarized failure case information and condition indicator results. The results are presented and discussed in Section 3 and conclusions in Section 4.