| 张锐,刘婷婷,王燕,付俊淋,周卫斌,卜二军,王永霞,游国栋.基于FBSE-ESEWT的齿轮故障诊断方法[J].电子测量与仪器学报,2025,39(4):234-246 |
| 基于FBSE-ESEWT的齿轮故障诊断方法 |
| Gear fault diagnosis method based on FBSE-ESEWT |
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| DOI： |
| 中文关键词: 经验小波变换 傅里叶-贝塞尔级数 能量尺度空间 降噪 故障诊断 |
| 英文关键词:empirical wavelet transform Fourier-Bessel series energy scale space noise reduction fault diagnosis |
| 基金项目:中文基金项目内蒙古自治区重点研发和成果转化计划（2023YFJM0007）、内蒙古自治区自然科学基金（2024ZD26）项目资助 |
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| Author | Institution |
| Zhang Rui | School of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China |
| Liu Tingting | School of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China |
| Wang Yan | School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China |
| Fu Junlin | School of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China |
| Zhou Weibin | School of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China |
| Bu Erjun | Inner Mongolia Academy of Science and Technology, Hohhot 010010, China |
| Wang Yongxia | School of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China |
| You Guodong | School of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China |
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| 摘要点击次数: 34 |
| 全文下载次数: 57 |
| 中文摘要: |
| 针对齿轮故障诊断中采集到的振动信号常伴有噪声干扰且故障特征难以提取的问题，以傅里叶 贝塞尔级数展开（Fourier-Bessel series expansion，FBSE）为基础，提出了一种将FBSE和基于能量的尺度空间经验小波变换（energy scale space empirical wavelet transform, ESEWT）相结合的齿轮振动信号降噪方法，即FBSE-ESEWT。首先，将采集到的齿轮振动信号利用FBSE技术获得其频谱，以替代传统的傅里叶谱，接着凭借能量尺度空间划分法对获取的FBSE频谱进行自适应分割和筛选，以精确定位有效频带的边界点。随后通过构建小波滤波器组得到信号分量并进行重构，以减小噪声和冗余信息干扰；然后，为捕捉到更全面的特征信息将处理后的信号进行广义S变换得到时频图，输入2D卷积神经网络进行故障诊断验证算法可行性。通过对Simulink仿真信号和实际采集信号进行实验，结果表明，相对于原始经验小波变换（EWT）、经验模态分解（EMD）等方法，FBSE-ESEWT具有更好的降噪效果，信噪比提高了13.96 dB，诊断准确率高达98.03%。 |
| 英文摘要: |
| Aiming at the problem that vibration signals collected in gear fault diagnosis are often accompanied by noise interference and fault features are difficult to extract, based on Fourier-Bessel series expansion (FBSE). A noise reduction method of gear vibration signal, which combines FBSE and energy scale space empirical wavelet transform (ESEWT), is proposed. Firstly, the frequency spectrum of the acquired gear vibration signal is obtained by using FBSE technology to replace the traditional Fourier spectrum. Then, the obtained FBSE frequency spectrum is adaptive segmented and screened by using the energy scale space partitioning method to accurately locate the boundary points of the effective frequency band. Then the signal components are obtained by constructing wavelet filter banks and reconstructed to reduce noise and redundant information interference. Then, in order to capture more comprehensive feature information, the processed signal is transformed by generalized S-transform to obtain time-frequency graph, and 2D convolutional neural network is input for fault diagnosis to verify the feasibility of the algorithm. Through experiments on Simulink simulation signals and actual acquisition signals, the results show that compared with the original EWT, EMD and other methods, FBSE-ESEWT has better noise reduction effect, the signal-to-noise ratio is increased by 13.96 dB, and the diagnosis accuracy is up to 98.03%. |
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