## 5 Conclusions  
The rapid growth in the use of wind turbines combined with their operation under severe loading conditions has increased the need for efficient condition monitoring technologies. Early fault detection is important for wind farm operators in order to improve predictive maintenance strategies, reduce unexpected downtime and associated repair costs. Continuously variable operation of the wind turbines and complexity of the multi-stage gearboxes employed makes it very challenging to detect developing faults particularly during the early stages of evolution.  
The cyclostationary technique has been proven to be a very effective tool to detect faults in rotating machinery. However as the complexity of the system increases, the performance of this technique degrades and the results get contaminated with various artefacts coming from different parts of the system.  
In this paper the Ensemble Empirical Mode Decomposition technique was used to precondition the vibration signal prior to perform the cyclostationary technique. It has been shown that this can improve the performance of the cyclostationary technique and reduce the contamination in the resulting analysed signals.  
The Ensemble Empirical Mode Decomposition technique is a very powerful method that has not been fully explored in the field of wind turbine condition monitoring. The present study has shown the potential of this technique to be employed in conjunction with other techniques to enhance  
Figure 10: Conventional techniques applied to vibration signal recorded from planetary gearbox of wind turbine with bearing and gear defect. (**a**) The Cepstral analysis on the original signal with red dashed line on 7.1 ms representing the gear-mesh of the planets at 140.8 Hz. (**b**) The Hanning windowed single sided FFT amplitude of both original signal and its Hilbert transform with the red dashed line on 140.8 Hz corresponding to the plants gear-mesh. (**c**) Same as (**a**) with the red dashed line showing BPFI of the bearing of planets at 114 ms or 8.8 Hz. (**d**) Same as (**b**) with the red dashed line showing BPFI of the bearing of planets at 8.8 Hz.  
the overall performance of various signal processing techniques in wind turbine gearbox condition monitoring. The ability of this technique to separate the signal into its intrinsic modes, as it was demonstrated in this paper, opens new opportunities in advanced signal processing that are yet to be explored in more depth.  
[PERSON] and [PERSON] developed and carried out the signal processing methodology employed in the study, [PERSON] organised and led the experiments carried out. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by European Union's Seventh Framework grant number 322430.  
The authors would like to thank the several industrial partners anonymised in this paper due to confidentiality reasons for their support. The authors would also like to thank TWI Limited, Brunel University and INGETEAM service for their assistance with some of the tests. The authors are indebted to the EC for partially funding this research as part of the OPTIMUS FP7 industrial collaborative project.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BPFI & Ball Pass Frequency of Inner race \\ BPFO & Ball Pass Frequency of Outer race \\ BSF & Ball Spin Frequency \\ EEMD & Ensemble Empirical Mode Decomposition \\ EMD & Empirical Mode Decomposition \\ FTF & Fundamental Train Frequency \\ GM & Gear-mesh \\ GAPF & Gear Assembly Phase Frequency \\ IMF & Intrinsic Mode Function \\ \end{tabular}