Abstract
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.
Highlights
Wind energy is one of the most significant sources of clean energy to replace fossil energy in Europe, with 63% of the investment of renewable projects in Europe corresponding only to wind energy projects [1] and 15% of the Europe Union electricity demand [2]
The values for healthy scenario (HS) and damage 1 scenario (D1) are significantly close in comparison to the values for D2, which are clearly separated to the aforementioned scenarios
Results obtained for data at 10 rpm can be seen in Figure 6a, where the Spectral entropy (SE) values for HS, D1, and D2, along with their affine linear regression are shown
Summary
Wind energy is one of the most significant sources of clean energy to replace fossil energy in Europe, with 63% of the investment of renewable projects in Europe corresponding only to wind energy projects [1] and 15% of the Europe Union electricity demand [2]. To increment the energy share of wind energy on the total installed power capacity, one strategy is to decrease the energy costs [3]. Since up to 30% of a megawatt price from a wind farm is used to cover operation and maintenance (O&M) fixed cost [4], strategies for its reduction can significantly impact to the price of wind energy (and its adoption). One strategy is the condition based maintenance (CBM), which helps to considerably decrease the cost of wind turbine O&M [5,6]. Because CBM relies on fault diagnosis and prognosis models to automate the evaluation of real-time data, more precise and reliable models are being developed to enhance the maintenance plans for all wind turbine components. This study focuses on pitch bearings, as their breakdown cause considerable downtime and economic losses [7]
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