Wind Turbine Bearing Basics

Getting acquainted with wind turbine bearings is essential to anyone who works in the wind energy industry. A basic knowledge of the subject is necessary to ensure that you can properly identify, analyze and repair defects that may arise. In this article we'll go through the basics of wind turbine bearings, including how to measure their load distribution, how to diagnose faults, and how to predict the remaining useful life.

Fundamentals of wind turbine bearings

Several methods have been developed to evaluate the remaining useful life (RUL) of wind turbine bearings. These techniques can help to reduce downtime costs. These include model-based and data-driven methods. However, the latter requires the development of suitable health indicators.

As wind turbines become more complex, the use of data-driven approaches is growing. These techniques attempt to construct physical degradation models based on data gathered from a wind turbine. In addition, they can also support the advanced maintenance of a wind turbine.

The main bearing of a wind turbine serves a critical role by supporting the rotor. It is essential to design bearings that are not prone to truncation. In order to avoid truncation, a radial preload is applied on the rollers.

Load distribution

Among the key load carrying components of wind turbines are the main bearings. These carry loads from the main shaft and the wind wheel. The loads are defined using the hub coordinate system. The axis of the hub is coaxial with the axis of the rotating shaft. The bearing ring is mounted in the stator shell of the generator.

Load distribution research is important for understanding and assessing other bearing performance indicators. A good understanding of aerodynamic loads can be used in the design process and in operations. Several different methods have been implemented for calculating blade and bearing equivalent loads.

Diagnosis of faults

Identifying faults in wind turbine bearings is a challenging task. The reason for this is that the vibration signal is not sufficient to identify the fault location. Due to the slow rotation speed of the bearings, the signals are weak. Heavy noise disturbances also obscure the signals.

Wind turbine bearings are subjected to a wide range of wear and tear. This can be due to high localised stresses caused by surface damage, or excessive loading. Spalling is another common type of fault. The process involves flaking of the bearing material.

The conventional method for diagnosing bearing faults in wind turbines involves the use of signal processing techniques. The most commonly used signals are MB temperatures. These are extracted by using the empirical Wavelet Transform (EWT). This technique is widely regarded as a powerful tool for mechanical fault diagnosis.

Predicting the Remaining Useful Life

Using an accurate bearing health indicator can help to predict the remaining useful life (RUL) of the wind turbine gearbox. This is important for condition-based maintenance and the early detection of faults. It also can reduce the cost of maintenance and downtime.

A number of studies have been conducted on the topic. The most common methods include machine learning and data-driven approaches. While they are effective, they also face some challenges. In addition, the accuracy of the model may vary depending on the type of bearing.

During the recent decade, wind power generation has increased significantly. The market for wind energy has become a key trend in green energy. Among the most fragile components in a wind turbine gearbox are the bearings. They contribute to about 80 percent of total downtime.

Early warning under predictable conditions

Detecting faults in a wind turbine is a difficult task. Especially when the root cause of the problem is unclear. However, there are various techniques available to diagnose a machine condition and to find the reason for a failure. These include the use of a sensor and the analysis of data to detect faults.

The most established method is the vibration-based CMS. This system uses the order spectrum of a turbine's active power output to detect and decipher failure modes. The spectral order of the harmonics is determined to identify the peaks that may indicate a bearing fault.

Another technique is the use of a fuzzy logic system to detect a developing fault. This technique has been proposed by several researchers.

Methods for mitigating failures

Increasing the capacity of wind turbines puts new challenges on rolling bearings in the drive train. Several ISO standards are in use to ensure the reliability of wind turbines. These standards underpin the wind turbine bearing design specifications.

The main bearings of wind turbines perform a critical role of supporting the rotor. They also play a significant role in power generation. They are subjected to a wide range of operating conditions. Normally, the loads generated by the rotor are moderate, but specific conditions can result in excessive loading and disturbed kinematics.

Many failure mechanisms are self-perpetuating. For example, white etching cracks, known as brittle or early failure modes, are caused by damage to the bearings. Other damage mechanisms can involve spalling, pitting, and micro-pitting.