The future of wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly offshore to benefit stronger and more uniform wind conditions. Cost of operation and maintenance of offshore wind turbines is among 15-35% of the total cost. From this, 80% comes from unplanned maintenance due to different faults in the wind turbine components. Thus, an auspicious way to contribute to the increasing demands and challenges is by applying low-cost advanced fault detection schemes. This work proposes a new method for fault detection of wind turbine actuators and sensors faults in variable-speed wind turbines. For this purpose, time domain signals acquired from the operating wind turbine are converted into two-dimensional matrices to obtain gray-scale digital images. Then, the image pattern recognition is processed getting texture features under a multichannel representation. In this work, four types of texture features are used: statistical, wavelet, granulometric and Gabor features. Then, the most significant features are selected with the conditional mutual criterion. Finally, the fault detection is performed using an automatic classification tool. In particular, a 10-fold cross validation is used to obtain a more generalized model and evaluate the classification performance. In this way, the healthy and faulty conditions of the wind turbine can be detected. Coupled non-linear aero-hydro-servo-elastic simulations of a 5MW offshore type wind turbine are carried out for several fault scenarios. The results show a promising methodology able to detect the most common wind turbine faults.