## 5 Conclusions  
A hybrid fault detection system based on SCADA alarm logs and an anomaly detection autoencoder were presented and validated on a fleet of more than 100 wind turbines, from four different manufacturers, located in different parts of Europe. Real operating data has been used and most of the raised alarms corresponded to problems related to the generator that required the substitution of the component or some parts of it (bearings, brushes).  
A detailed explanation of the most critical windfarm was presented to show how the methodology can be applied in practice and the kind of analyses that were carried out to corroborate the results.  
It has been observed that the alarm counter is a valid tool to distinguish faulty turbines from healthy ones. That being said, the alarm counter alone cannot anticipate all failures. The fusion of anomalies and alarms information complements the individual approaches, providing a more reliable system.  
All five failures that occurred during the test phase were correctly detected. Of the two \"false positive\" predictions that were obtained, detailed analyses suggested that they are likely early fault detections, rather than errors. Ultimately, this methodology provides windfarm operators a reliable tool to assess the health of generators and improve operation and maintenance of the turbines.  
The results of the autoencoder as an anomaly detector were compared with other common algorithms in the literature, such as isolation forest and one-class support vector machine. The results showed that while the other two algorithms provide acceptable results, autoencoders are more confident in their predictions in cases where alarm information cannot help so much with separating faulty from healthy turbines. Autoencoders, having more tunable parameters and allowing for more elaborated structures, are capable to better interpret non-linear data, such as that of a turbines generator. Additionally, the overall methodology was tested against a normality model, and the results clearly showed that the proposed solution ranks better for all the tracked statistics.  
This research contributes to present a novel methodology that makes use of data analysis techniques for anomaly detection and consolidates the results, merging the anomaly predictions with information from the alarm system. The large size of the datasets and its diversity contribute to prove the approach as a general solution that can work well in real-life conditions and is not only applicable to a niche of turbines.  
Different network architecture, including temporal information and denoising autoencoders, should be explored in future research to boost the accuracy of the system. Interpretability of results is a key aspect that requires further improvements to ensure acceptance of this methodology in the market.  
Conceptualization, M.B. and J.J.C.; methodology, M.B., J.J.C. and C.K.; software, M.B., J.J.C. and C.K.; validation, M.B., J.J.C., J.C. and C.K.; formal analysis, M.B., J.J.C. and J.C.; investigation, M.B.; resources, J.C.; data curation, M.B. and J.C.; writing--original draft preparation, M.B.; writing--review and editing, J.C.; visualization, M.B.; supervision, J.J.C. and J.C.; project administration, J.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors want to acknowledge the support of the other colleagues at SMARTIVE, [PERSON] and [PERSON], for their support and technical advice in the design of this experiment and the analysis of the results. Additionally, the authors would like to acknowledge the work of the anonymous reviewers, who provided useful notes and critiques to improve the overall quality of the article.  
The authors declare no conflict of interest.