To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.

Despite its influence on wind energy service life, condition-based maintenance is still challenging to perform. For offshore wind farms, which are placed in harsh and remote environments, damage detection is critically important to schedule maintenance tasks and reduce operation and maintenance costs. One critical component to be monitored on a wind turbine is the pitch bearing, which can operate at low speed and high loads. Classical methods and features for general purpose bearings cannot be applied effectively to wind turbine pitch bearings owing to their specific operating conditions (high loads and non-constant very low speed with changing direction). Thus, damage detection of wind turbine pitch bearings is currently a challenge. In this study, entropy indicators are proposed as an alternative to classical bearing analysis. For this purpose, spectral and permutation entropy are combined to analyze a raw vibration signal from a low-speed bearing in several scenarios. The results indicate that entropy values change according to different types of damage on bearings, and the sensitivity of the entropy types differs among them. The study offers some important insights into the use of entropy indicators for feature extraction and it lays the foundation for future bearing prognosis methods.

In this paper an Ant Colony optimization (ACO) algorithm is proposed as tuning mechanism for Proportional-Integral-Derivative(PID) controller of a direct current (DC) motor from a robotic arm. Firstly, the model of the DC motor, PID and ACO algorithm are described. Secondly, it is performed the PID tuning using Ziegler-Nichols (ZN), manual tuning and the ACO algorithm. Finally, the experimental results of PID and tuning methods are compared and discussed.

The continuous increase of energetic consumption worldwide has been a necessity for the development of human society, which is cover historically with fossil fuels. The use and negative contribution of fossil fuels to the planet have been evident with the climate change phenomenon. This has caused the awareness of society and institutions, which understand the unfeasibility of the electricity generation by conventional means (fossil fuels) in the medium and long term. For this reason, sustainable and efficient energy sources are becoming a fundamental objective at present. Wind power is one of these options since it has economically viable costs. A large part of the costs associated with offshore wind turbines is the operation and maintenance activities, which can be improved. A strategy is the monitoring of the condition, which allows extending the useful life of the components of the wind turbine. Within the components present in a wind turbine, the pitch bearings are one of the critical elements because of their frequent failure, along with the time and logistics involved in their replacement. These issues encourage the realization of the present revision of the techniques used in condition monitoring. The identification of these topics allows the visualization and proposition of future lines of research. Key words: monitoring, wind turbine, pitch bearing, vibration, acoustic emission.