A review of bearing failure Modes, mechanisms and causes

Xu, Funing; Ding, Ning; Li, Nan; Liu, Long; Hou, Nan; Xu, Na; Guo, Weimin; Tian, Linan; Xu, Huixia; Lawrence Wu, Chi-Man; Wu, Xiaofeng; Chen, Xiangfeng

doi:10.1016/j.engfailanal.2023.107518

Cited by 29 publications

(12 citation statements)

References 122 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main shaft bearing is one of the core components of the wind turbine, and its role is mainly to support the wind turbine, bear the load of the wind turbine, and enable the wind turbine to operate smoothly [35]. The main forms of wear of the wind turbine main shaft bearings are abrasive wear, fatigue wear, and oxidative wear [36].…”

Section: Main Forms Of Wear On Wind Power Bearingsmentioning

confidence: 99%

“…Lubrication is one of the effective ways to reduce bearing failure, it can not only reduce bearing friction and wear but also avoid damage to other parts, reducing the failure rate of wind turbines which plays an important role [34,87,88]. According to the statistics, the bearing failure rate due to lubrication failure is up to 70%, and the causes can include unqualified lubricant quality, premature lubricant failure, lack of lubricant, over-lubrication, and incorrect lubricant selection [35,89,90]. At the same time, bearing failures in wind turbines, which require downtime for repairs and may require replacement parts, increase overall maintenance costs and cause significant losses in power production [91,92].…”

Section: Analysis Of Common Lubrication Problems In Wind Power Bearingsmentioning

confidence: 99%

“…Analyzing the lubrication status of bearings is essential to maintaining mechanical equipment [35,172]. Scientific researchers, industry and commerce, in order to make bearings in a long-term good lubrication state, will use traditional methods and new technologies.…”

Section: Boundary Lubrication 1~50 Nmmentioning

confidence: 99%

See 2 more Smart Citations

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

Peng,

Zhao,

Shangguan

et al. 2023

Coatings

View full text Add to dashboard Cite

With the significant penetration of wind generation, wind turbines require higher and higher lubrication performance for bearings. To improve the lubrication performance of wind power bearings, this study takes wind power bearings as the research object and comprehensively analyzes the wear forms of wind power bearings as well as intelligent lubrication methods. Firstly, the main roles and wear forms of wind turbine bearings are sorted out and analyzed. Secondly, the common lubrication problems of wind power bearings are analyzed from the bearing grease selection, lubrication mode, and lubrication status, highlighting the important influence of lubrication on bearings. Thirdly, the wind turbine bearing wisdom lubrication method research and organization, mainly including the wind power generation bearing lubrication materials, lubrication devices and monitoring methods, and other issues of research and analysis. Finally, current challenges and future development directions are summarized, which are designed to provide theoretical reference and technical support for the related research and engineering practice in the field of wind power engineering.

show abstract

Section: Main Forms Of Wear On Wind Power Bearingsmentioning

confidence: 99%

Section: Analysis Of Common Lubrication Problems In Wind Power Bearingsmentioning

confidence: 99%

See 1 more Smart Citation

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

Peng,

Zhao,

Shangguan

et al. 2023

Coatings

View full text Add to dashboard Cite

show abstract

“…Both phenomena can result in abnormal oil film distribution (Zhu et al, 2023). Consequently, this can cause wear, peeling and pitting damage to the bearings (Xu et al, 2023), thus affecting the regular operation of the machinery.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review of non-destructive methods for bearing lubricating oil film thickness measurement

Shang,

Sun,

Cai

2024

ILT

View full text Add to dashboard Cite

Purpose The purpose of this study is to investigate the application of non-destructive testing methods in measuring bearing oil film thickness to ensure that bearings are in a normal lubrication state. The oil film thickness is a crucial parameter reflecting the lubrication status of bearings, directly influencing the operational state of bearing transmission systems. However, it is challenging to accurately measure the oil film thickness under traditional disassembly conditions due to factors such as bearing structure and working conditions. Therefore, there is an urgent need for a nondestructive testing method to measure the oil film thickness and its status. Design/methodology/approach This paper introduces methods for optically, electrically and acoustically measuring the oil film thickness and status of bearings. It discusses the adaptability and measurement accuracy of different bearing oil film measurement methods and the impact of varying measurement conditions on accuracy. In addition, it compares the application scenarios of other techniques and the influence of the environment on detection results. Findings Ultrasonic measurement stands out due to its widespread adaptability, making it suitable for oil film thickness detection in various states and monitoring continuous changes in oil film thickness. Different methods can be selected depending on the measurement environment to compensate for measurement accuracy and enhance detection effectiveness. Originality/value This paper reviews the basic principles and latest applications of optical, electrical and acoustic measurement of oil film thickness and status. It analyzes applicable measurement methods for oil film under different conditions. It discusses the future trends of detection methods, providing possible solutions for bearing oil film thickness detection in complex engineering environments.

show abstract

“…Serving as essential support components for rotating parts, rolling bearings find widespread application in various fields, including industrial manufacturing, wind power, rail transit, and aerospace [ 1 , 2 , 3 , 4 ]. However, the prevalence of uninterrupted loading in harsh working conditions substantially elevates the risk of bearing failures [ 5 , 6 , 7 ]. Traditional approaches to bearing fault diagnosis typically entail the analysis of bearing signals in the time and frequency domains, often relying on manual techniques to discern fault features [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network with Attention Mechanism and Visual Vibration Signal Analysis for Bearing Fault Diagnosis

Zhang,

Wei,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

Bearings, as widely employed supporting components, frequently work in challenging working conditions, leading to diverse fault types. Traditional methods for diagnosing bearing faults primarily center on time–frequency analysis, but this often requires expert experience for accurate fault identification. Conversely, intelligent fault recognition and classification methods frequently lack interpretability. To address this challenge, this paper introduces a convolutional neural network with an attention mechanism method, denoted as CBAM-CNN, for bearing fault diagnosis. This approach incorporates an attention mechanism, creating a Convolutional Block Attention Module (CBAM), to enhance the fault feature extraction capability of the network in the time–frequency domain. In addition, the proposed method integrates a weight visualization module known as the Gradient-Weighted Class Activation Map (Grad-CAM), enhancing the interpretability of the convolutional neural network by generating visual heatmaps on fault time–frequency graphs. The experimental results demonstrate that utilizing the dataset employed in this study, the CBAM-CNN achieves an accuracy of 99.81%, outperforming the Base-CNN with enhanced convergence speed. Furthermore, the analysis of attention weights reveals that this method exhibits distinct focus of attention under various fault types and degrees. The interpretability experiments indicate that the CBAM module balances the weight allocation, emphasizing signal frequency distribution rather than amplitude distribution. Consequently, this mitigates the impact of the signal amplitude on the diagnostic model to some extent.

show abstract

A review of bearing failure Modes, mechanisms and causes

Cited by 29 publications

References 122 publications

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

A comprehensive review of non-destructive methods for bearing lubricating oil film thickness measurement

Convolutional Neural Network with Attention Mechanism and Visual Vibration Signal Analysis for Bearing Fault Diagnosis

Contact Info

Product

Resources

About