# Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis
## 5 Conclusions  
In this work an ensemble method for main bearing fault diagnosis has been proposed, implemented, and validated on a real under-production wind park composed by 18 WTs. The ensemble combines a normality model at WT level (i.e., a specific model is trained for each WT in the park) with an anomaly detection model at wind park level by using only SCADA data and past work order logs.  
The stated ensemble method only requires healthy data to be trained (since it is not a supervised approach), and thus the methodology can be applied to any WT, even when there are no past records about the fault under study. The obtained results show that for the same value of the DT the best performance metrics are clearly obtained with the ensemble method in comparison to the single methods. Furthermore, the proposed strategy is able to warn at least 5 months in advance (giving enough time to the wind park operator to organize logistics and minimize downtime) the three main bearing faults present in the wind park during the test set, with few false alarms.  
The DT has a great influence on the final results, thus it is strongly advised that the wind park operator analyzes the cost of unnecessary checks (due to false alarms) with respect to the savings of early warning of the main bearing fault to decide the best DT to be employed.  
Two future work directions are envisioned. First, the extended isolation forest (EIF) presented in [30] will be incorporated to investigate the possible improvement in the results. Second, gearbox faults will be studied, as gearboxes tend to fail prematurely in WTs, and their replacement is very costly (the outage can last between a few days to months, depending on crane and parts availability). The wind park SCADA data and work order logs used in this research also contain gearbox faults, thus future work will address this type of fault, taking as starting point the ensemble method proposed in this work.  
All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by the Spanish Agencia Estatal de Investigacion (AEI) - Ministerio de Economia, Industria y Competitividad (MINECO), and the Fondo Europeo de Desarrollo Regional (FEDER) through the research project DPI2017-82930-C2-1-R; by the Generalitat de Catalunya through the research project 2017 SGR 388; by Centro para el Desarrollo Tecnologico Industrial, grant number CDTI-IDI 20191294; and by Agencia de Gestio d'Ajuts Universitaris i de Recerca (AGAUR), grant number DOCTORADO AGAUR-2017-DI 004.  
Not applicable.  
The data required to reproduce these findings cannot be shared at this time as it is proprietary.  
The authors express their gratitude and appreciation to the Smartive company ([[http://smartive.eu/](http://smartive.eu/)]([http://smartive.eu/](http://smartive.eu/)), accessed on 5 July 2021), as this work would not have been possible without their support and cededed wind park data.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript, and in the decision to publish the results.