| Related citation: | Weizhong Zhang,Xiaoan Yan,Maoyou Ye,Xing Hua,Dong Jiang.An Improved Wind Turbine Bearing Fault Diagnosis Method Basedon POSGMD and ICNN Under Strong Noise Scenarios[J].Journal of Harbin Institute Of Technology(New Series),2026,33(1):1-19.DOI:10.11916/j.issn.1005-9113.2024102. |
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| Author Name | Affiliation | | Weizhong Zhang | School of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, China | | Xiaoan Yan | School of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, China | | Maoyou Ye | School of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, China | | Xing Hua | School of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, China | | Dong Jiang | School of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, China |
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| Abstract: |
| Owing to the harsh conditions, wind turbine bearings are prone to faults, and the resulting fault information is easily submerged by strong noise disturbance, making conventional diagnosis challenging. Therefore, this study presents an innovative bearing fault diagnosis approach predicated on Parameter-Optimized Symplectic Geometry Mode Decomposition (POSGMD) and Improved Convolutional Neural Network (ICNN). Firstly, assisted by the relative entropy-based adaptive selection of embedding dimension, a POSGMD is presented to adaptively decompose the collected bearing vibration signals into various Symplectic Geometry Components (SGC), which can solve the problem of manual selection of the embedding dimension in the raw Symplectic Geometry Mode Decomposition (SGMD). Meanwhile, the signal reconstruction on the decomposed SGC is conducted based on kurtosis-weighted principle to obtain the reconstructed signals. Subsequently, the Continuous Wavelet Transform (CWT) of the reconstructed signals is calculated to generate the corresponding time-frequency images as sample set. Finally, an ICNN is introduced for model training and automatic recognition of bearing faults. Two case studies are used to validate the presented method's efficacy. Comparing the presented method with traditional fault diagnosis methods, experimental results show that it can achieve greater identification accuracy and superior anti-noise resilience. This work provides a practical and effective solution for fault diagnosis in wind turbine bearings, contributing to the timely detection of faults and the reliable operation of wind turbines or other rotational machinery in industrial applications. |
| Key words: symplectic geometry mode decomposition convolutional neural network deep learning rolling bearing fault diagnosis anti-noise robustness |
| DOI：10.11916/j.issn.1005-9113.2024102 |
| Clc Number:TM315,TH133 |
| Fund: |
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| Descriptions in Chinese: |
| 强噪声环境下基于POSGMD和ICNN的改进风力发电机轴承故障诊断方法 章伟忠，鄢小安，叶茂友，花兴，姜东 南京林业大学 机械电子工程学院，南京 210037 摘要：恶劣的工作条件容易使风力发电机轴承发生故障。由于产生的故障信息很容易被强噪声干扰淹没，这使传统的故障诊断变得具有挑战性。因此，本研究提出一种基于参数优化辛几何模态分解（POSGMD）和改进卷积神经网络（ICNN）的创新轴承故障诊断方法。首先，在基于相对熵的的辅助下，提出一种POSGMD，将收集到的轴承振动信号自适应分解为不同数量的辛几何分量（SGC），可以解决原始辛几何模态分解（SGMD）中手动选择嵌入维数的问题。同时，基于峭度加权原理对分解后的辛几何分量进行信号重构，得到重构信号。随后，对重构信号进行连续小波变换，以生成相应的时频图像作为样本集。最后，引入ICNN进行故障诊断。本文采用两个案例用于验证所提出方法的有效性。实验结果表明，与传统的故障诊断方法相比，该方法具有更高的识别精度和更强的抗噪能力。这项工作为风力发电机轴承的故障诊断提供一种实用有效的解决方案，有助于及时检测故障，确保风力发电机或其他旋转机械在工业应用中的可靠运行。 关键词：辛几何模态分解；卷积神经网络；深度学习；滚动轴承；故障诊断；抗噪声鲁棒性 |
