## 1 Introduction  
With the wind energy global installed capacity reaching 651 GW in 2019 [1], effective remote condition monitoring and predictive maintenance strategies for wind turbines are becoming more significant attracting increased attention. Wind turbines are typically expected to have a design lifespan of 20-25 years. However, they rarely meet this target without major overhauls, particularly in their drivetrain [2]. Gearbox faults and failures take considerable time to repair. Hence, they result in major downtime and loss of production capacity leading to increased maintenance costs for wind farm operators [3; 4; 5; 6].  
Direct drive wind turbine designs remove the need for a gearbox. However, the power converters of direct drive wind turbines are among the most frequent failing components which cumulatively result in excessive downtime [7; 8]. Thus, the failure rate of power electronics in direct drive wind turbines greatly exceeds the failure rate of the gearbox in geared wind turbines [8]. Nonetheless, due to the advantages and disadvantages of each design, technology forecasts suggest that both geared and direct drive wind turbines will continue to be used in the future [9].  
Unlike simple gearboxes used in conventional steady-state machinery, the vibration signals recorded from the gearbox of a wind turbine can be contaminated resulting in erroneous interpretation of the data acquired. Wind turbine gearboxes have very complex designs, comprising many different gears and bearings resulting in the vibration of different parts of the gearbox to superimpose in the recorded vibration signal with multiple frequency and amplitude modulations occurring. Hence, it is very challenging to detect a developing fault in its early stages using conventional signal processing techniques such as Fast Fourier Transform or RMS [10]. Moreover, once a fault is detected it is extremely important from a maintenance point of view to evaluate the severity. Identifying a defect alone is not sufficient for a wind farm operator in order to decide if maintenance is required. In addition, if it is deemed that maintenance is required it is very important to identify when it should be carried out without risking loss of production and expensive outages. Gearboxes are rarely available as spare parts and therefore it can take several months before a spare gearbox can become available. Therefore, it is critical for wind farm operators to pinpoint the exact gearbox component affected by the fault and quantify its severity in order to accurately plan maintenance without the risk of loss of production.  
The cyclostationary signal processing technique is a very powerful tool for fault detection in rotating machinery. Moreover, it can be utilised to evaluate the severity of developing faults in rolling elements and bearings. However, as it has been suggested by [PERSON] and [PERSON] [11], this technique is not very successful in complex systems when multiple gears and bearings are present.  
Here we have investigated the suitability of combining two advanced signal processing techniques for the purpose of fault detection in the gearbox of wind turbines. These techniques are known as Cyclic Spectral Analysis and Ensemble Empirical Mode Decomposition (EEMD), which will be explained in more details in the next section. It should be stressed, that wind turbines operate in variable loading conditions and therefore, the accurate evaluation of the severity of faults is very challenging [12]. Moreover, false indications of non-existent faults are not uncommon when using conventional signal processing techniques.