A Review of Research on Wind Turbine Bearings’ Failure Analysis and Fault Diagnosis
Abstract
:1. Introduction
2. Wind Turbine Bearings’ Failure Patterns Analysis
- Plastic deformation
- (a)
- General surface plastic deformation
- (b)
- Local surface plastic deformation
- Indentation
- Bumping injuries
- Bruising
- Scratch
- Wear
- Cracks and fractures
- (a)
- Forced fracture
- (b)
- Fatigue fracture
- (c)
- Thermal cracks
- Electric erosion
- (a)
- Excessive voltage
- (b)
- Excessive current/current leakage
- Lubricant
- (a)
- Insufficient lubrication
- (b)
- Over lubrication
- (c)
- Ineffective lubrication
- (d)
- Lubricant contamination
- Contact fatigue
- (a)
- Surface origin
- (b)
- Sub-surface origin
- Engineering failure
- (a)
- Manufacturing factors
- Bearing structure design
- Material quality
- Heat treatment quality
- (b)
- Operating factors
- Bearing selection
- Installation
- Lubrication
- Sealing
3. Wind Power Bearings’ Fault Diagnosis Mechanism and Process
4. Research on Wind Power Bearing Fault Diagnosis Technology
4.1. Fault Diagnosis of Wind Turbine Bearings Based on Spectrum Analysis
4.2. Fault Diagnosis of Wind Turbine Bearings Based on Wavelet Analysis
4.3. Fault Diagnosis of Wind Turbine Bearings Based on Artificial Intelligence
5. Summary and Conclusions
5.1. Research on Failure Analysis for Wind Turbine Bearings
- As can be seen from earlier studies, researchers have examined wind turbine bearing failure issues in great detail and have a thorough understanding of the various bearing failure modes and causes. The cause of early bearing failure is still not fully understood, and most studies assessing the mode of bearing failure in wind turbines have been validated only under ideal laboratory conditions. In addition, because of the complexity of bearing failure modes, it is recommended that more basic work be completed to understand the root cause of the failures.
- In terms of the tribological failures of wind turbine bearings, comparatively less attention has been focused on main shaft bearings, pitch bearings, and generator bearings. Therefore, more basic research on bearings of such components is needed to understand their failure mechanisms and damage modes.
- In terms of the formation mechanism of the mode of bearing failure, while some progress has been made in the form of failure and maintenance measures for wind power bearings, the formation mechanism of the mode of failure is not yet clear. An in-depth combination of bearings’ structure and working characteristics is needed in the future. Starting from the aspects of coatings, lubricants, and heat treatment, corresponding research will be conducted to analyze the influence of different factors on bearing failure modes and reduce maintenance costs.
- The root cause of premature bearing failure is primarily related to lubrication and the materials used. During manufacturing, installation, operation, and maintenance, the quality of the components should be controlled to avoid breaking parts and debris entering the bearings. In addition, the conditions of the lubricant, including the temperature and color, should be closely monitored to ensure better lubrication.
- Regarding the identification of the failure modes of wind power bearings, most of the current research is directed towards the identification of singular faults. However, in practice it is usually a compound of multiple faults, a more complex failure mode, which is also an important direction for follow-up studies.
- With the widespread presence of offshore and large-scale wind turbines, a thorough database of wind power bearing failures is indispensable. By diversifying the content of the bearing failure knowledge base to handle natural damage and other failure types, interoperability of failure data can be accomplished.
5.2. Research on Bearing Fault Detection Methods for Wind Turbines
- Failure of wind turbine bearings can cause a sequence of changes in physical characteristic quantities, while a single physical characteristic quantity could also be caused by several failures. Therefore, the failure of bearings in different parts and the variability of different units should be combined. In addition, the data of multi-characteristic quantities are integrated and analyzed to seek the features of the failure data of the bearings in each part and its variation patterns.
- At present, wind power bearing fault diagnosis is still focused on theoretical aspects, while in the practical domain there will be noise, temperature, and other factors that can affect the judgment outcome. Therefore, various factors should be considered to accurately identify the location and type of faults.
- In the area of fault diagnosis, a point-to-point bearing dynamic data monitoring system should be established. Fault data for wind power bearings generally comes from SCADA systems. However, such systems have a low information sampling frequency, and most diagnostics are off-line analysis of steady-state signals. Therefore, it is necessary to build a dedicated dynamic bearing fault detection system based on the real-time operation of wind turbines.
- Achieving all-round information fusion for bearing fault diagnosis is a crucial future research direction, as the current non-stationary signal analysis method still has many urgent problems in practical application. Therefore, the advantages of various disciplines such as mathematics, material science, mechanics, and artificial intelligence should be effectively integrated into fault diagnosis to further promote fault diagnosis research.
- Current methods for bearing fault signal processing in wind turbines extract features by analyzing the bearing vibration signal measured from a single sensor and thus suffer from many problems. The use of multiple sensors to collect bearing operation data at various measurement points can obtain additional information and increase the accuracy and robustness of fault diagnosis. Thus, multi-sensor-based feature fusion techniques are the future trend in the field of fault diagnosis.
- Spectral analysis methods refer to a process of decomposing signals by Fourier transform and expanding them into frequency functions in frequency order and then investigating and manipulating the signals in the frequency domain. Spectral analysis techniques typically used for wind power bearing diagnostics include FFT power spectroscopy, cepstrum spectroscopy, refined spectroscopy, etc. By performing an analysis of the power spectrum and cepstrum of the signal, the specific fault of the system can be located. Defect diagnostics for wind power bearings are currently widely used using spectral analysis, but spectral map analysis is still not accurate enough. Therefore, the next stage is to focus on the creation of intelligent spectral analysis systems and the intelligent identification of spectral analysis using neural networks.
- Wavelet analysis, a signal processing technique for time–frequency analysis, helps resolve the conflict between the time and frequency resolution of classical Fourier analysis in the detection of faults in wind turbine bearings. Its primary use is wavelet decomposition, which successfully separates fault signals from bearing vibrations by choosing appropriate wavelet and scale parameters. The problem sites of the bearing vibrations are then identified by comparing the energy distribution in each frequency band. Wavelet-analysis-based diagnosis of wind power bearings is a useful technique for signal denoising and feature extraction. Bearing defect detection can be performed more effectively by combining wavelet analysis with additional techniques such as wavelet-neural networks, wavelet-support vector machines, and wavelet-fuzzy inference.
- Artificial-intelligence-based fault diagnosis for wind power bearings first requires training and self-learning of the problem and normal bearing operation data, and then realizing fault diagnosis through deduction and decision-making processes. To increase the accuracy of defect detection, artificial intelligence techniques make it possible to accomplish more difficult diagnostic tasks without human interaction. By creating a current network model, a huge data platform, and an intelligent cloud, artificial intelligence should be applied as a model for bearing fault diagnosis in the process of future development so that the operational status of wind turbine bearings can be assessed in advance and fault identification can be achieved.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Wear Type | Definition | Wear Phenomenon |
---|---|---|
Adhesive Wear | Adhesive wear is the mutual movement of materials on mutually rubbing surfaces, resulting in the transfer of substances onto the surfaces in relative motion. This further leads to a change in the morphology of the contact surfaces [53], as shown in Figure 4a. In the case of insufficient lubrication, the friction surface is prone to local deformation and damage phenomena due to the local friction temperature rise of the material. In severe cases, the surface metal will be locally spalled off, causing plastic deformation on the contact surface [54,55], as illustrated in Figure 4b. | Scuffing, seizing, flaking, skidding galling, and plastic deformation. |
Abrasive Wear | Abrasive wear is defined as the loss of material from a soft surface due to a slip when a tough surface or particle comes into contact with a softer surface. This is shown in Figure 4c. Differences in the coarseness and characteristics of its abrasive grains can lead to different degrees of material wear surface darkening [56]. Therefore, when abrasive particles such as dirt, sand, or flaking iron chips produce continuous wear that causes the bearing to become non-functional, it is termed as abrasive wear failure [57], as shown in Figure 4d. | Scratches, dents, indentations, bruises, plastic deformation, and chips. |
Corrosion Wear | Corrosion wear is the chemical reaction between the material on the bearing surface and the ambient medium, causing its interface to be damaged and failure. It mainly includes two categories of moisture corrosion and friction corrosion [58,59]. When the bearing surface is in contact with moisture, moisture corrosion will occur, as illustrated in Figure 4e. In addition, frictional corrosion is mainly caused by the metal of the bearing surfaces rubbing against each other. | Seizing, craters, cracks, pitting, and partial flaking. |
Fretting Wear | Fretting wear is caused by fretting corrosion and Brinell indentation of the contact surfaces caused by micro-sliding and rolling between the bearing contact surfaces. Among them, fretting corrosion occurs in the non-lubricated condition, which produces severe adhesion on the bearing surface. Brinell indentation, on the other hand, happens in the boundary lubrication situation on the bearing, with slight adhesion [60]. At the beginning, Brindle indentation presents a pseudo-indentation form. When the friction surface is formed without lubrication by the abrasive debris blocking the lubricant, it is gradually upgraded to fretting corrosion, as shown in Figure 4f. | Brinell indentation, chipping, pseudo indentation, and scuffing, notches. |
Bearing Type | Failure Mode |
---|---|
Main shaft bearing | Forced fracture; fatigue fracture; thermal cracks; adhesive wear; abrasive wear; plastic deformation; contact fatigue; lubricant failure; engineering failure. |
Generator bearing | Forced fracture; fatigue fracture; thermal cracks; adhesive wear; abrasive wear; plastic deformation; contact fatigue; lubricant failure; engineering failure. |
Pitch Bearing | Forced fracture; fatigue fracture; corrosion wear; fretting wear; plastic deformation; contact fatigue; lubricant failure; engineering failure. |
Yaw Bearing | Forced fracture; fatigue fracture; corrosion wear; fretting wear; plastic deformation; contact fatigue; lubricant failure; engineering failure. |
Gearbox bearing | Forced fracture; fatigue fracture; thermal cracks; adhesive wear; abrasive wear; plastic deformation; electrical erosion; contact fatigue; lubricant failure; engineering failure. |
Failure Location | Characteristic Frequency Calculation Formula |
---|---|
Inner ring | |
Outer ring | |
Rolling element | |
Cage |
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Peng, H.; Zhang, H.; Fan, Y.; Shangguan, L.; Yang, Y. A Review of Research on Wind Turbine Bearings’ Failure Analysis and Fault Diagnosis. Lubricants 2023, 11, 14. https://doi.org/10.3390/lubricants11010014
Peng H, Zhang H, Fan Y, Shangguan L, Yang Y. A Review of Research on Wind Turbine Bearings’ Failure Analysis and Fault Diagnosis. Lubricants. 2023; 11(1):14. https://doi.org/10.3390/lubricants11010014
Chicago/Turabian StylePeng, Han, Hai Zhang, Yisa Fan, Linjian Shangguan, and Yang Yang. 2023. "A Review of Research on Wind Turbine Bearings’ Failure Analysis and Fault Diagnosis" Lubricants 11, no. 1: 14. https://doi.org/10.3390/lubricants11010014