基于SSA-IWT-EMD的滚动轴承故障诊断方法

雷春丽; 焦孟萱; 樊高峰; 刘世超; 薛林林; 李建华

doi:10.13700/j.bh.1001-5965.2023.0174

基于SSA-IWT-EMD的滚动轴承故障诊断方法

doi: 10.13700/j.bh.1001-5965.2023.0174

1.
兰州理工大学机电工程学院，兰州 730050
2.
中石化英力士（天津）石化有限公司，天津 300280
3.
云南文山铝业有限公司，文山 663000

基金项目: 国家自然科学基金(51465035)；甘肃省自然科学基金(20JR5RA466)；兰州理工大学红柳一流学科建设项目

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通讯作者:
E-mail：li_jh@vip.sina.com

中图分类号: TH133.33
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出版历程
- 收稿日期: 2023-04-12
- 录用日期: 2023-06-30
- 网络出版日期: 2023-07-10
- 整期出版日期: 2025-04-30

Fault diagnosis method of rolling bearings based on SSA-IWT-EMD

1.
School of Mechanical and Electrical Engineering，Lanzhou University of Technology，Lanzhou 730050，China
2.
Ineos Sinopec Tianjin Petrochemicals Limited，Tianjin 300280，China
3.
Yunnan Wenshan Aluminum Co.，Ltd.，Wenshan 663000，China

Funds: National Natural Science Foundation of China (51465035); Natural Science Foundation of Gansu Province, China (20JR5RA466); Hongliu First-class Disciplines Development Program of Lanzhou University of Technology

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Corresponding author: E-mail：li_jh@vip.sina.com

摘要

摘要:
针对小波阈值降噪不充分及经验模态分解(EMD)特征频率提取不明显的问题，提出一种基于麻雀搜索算法-改进小波阈值-EMD(SSA-IWT-EMD)的滚动轴承故障诊断方法。引入2个调节因子，提出一种IWT函数，克服了传统软硬阈值的缺点，并运用SSA对其各参数进行全局寻优，实现滚动轴承信号降噪。提出一种综合指标P对EMD产生的分量进行选取重构，突出信号的故障特征信息。采用包络谱分析实现轴承的故障诊断。仿真和实测结果验证了所提方法的有效性；同时与单一指标选取分量的方法及文献方法进行对比，说明了综合指标P和所提方法具有更强的降噪能力及特征提取能力，包络谱幅值及倍频成分更明显，可以更好地实现对滚动轴承的故障诊断。
- 滚动轴承 /
- 改进阈值 /
- 综合指标 /
- 经验模态分解 /
- 故障诊断
Abstract:
Wavelet threshold denoising is insufficient, and feature frequency extraction of empirical mode decomposition (EMD) is unclear. To address these issues, a fault diagnosis method of rolling bearings based on sparrow search algorithm - improved wavelet threshold-EMD (SSA-IWT-EMD) was proposed. Firstly, two adjustment factors were introduced, and an IWT function was presented to overcome the shortcomings of traditional soft and hard thresholds. The SSA was used to globally optimize the parameters of the IWT to reduce the noise of rolling bearing signals. Secondly, a comprehensive index P was put forward to select and reconstruct the components generated by EMD, so as to highlight the fault feature information of the signals. Finally, the fault diagnosis of bearings was realized by envelope spectrum analysis. The simulation and experimental results verified the effectiveness of the proposed method. At the same time, the comparison with the single index component selection method and the literature method indicated that the comprehensive index P and the method proposed in this paper had stronger denoising ability and feature extraction ability, and the envelope spectrum amplitude and frequency doubling component were more obvious, which could better realize the fault diagnosis of rolling bearings.
- rolling bearing /
- improved threshold /
- comprehensive index /
- empirical mode decomposition /
- fault diagnosis

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图 1 小波阈值去噪流程

Figure 1. Flow chart of wavelet threshold denoising

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图 2 基于SSA-IWT-EMD的滚动轴承故障诊断流程

Figure 2. Flow chart of fault diagnosis of rolling bearings based on SSA-IWT-EMD

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图 3 冲击信号

Figure 3. Impact signal

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图 4 仿真信号

Figure 4. Simulation signal

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图 5 原始仿真信号包络谱

Figure 5. Envelope spectrum of original simulation signal

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图 6 SSA-IWT优化后仿真信号

Figure 6. Simulation signal optimized by SSA-IWT

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图 7 仿真信号EMD分解图

Figure 7. EMD diagram of simulation signal

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图 8 SSA-IWT-EMD仿真信号包络谱

Figure 8. Envelope spectrum of SSA-IWT-EMD simulation signal

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图 9 MFS机械故障试验平台

Figure 9. MFS test platform for mechanical fault

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图 10 试验轴承原始信号1

Figure 10. Original signal 1 of test bearing

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图 11 原始试验轴承信号1包络谱

Figure 11. Envelope spectrum of original signal 1 of test bearing

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图 12 SSA-IWT优化后轴承信号1时域图

Figure 12. Time domain diagram of signal 1 of bearing optimized by SSA-IWT

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图 13 试验轴承信号1 EMD分解图

Figure 13. EMD diagram of signal 1 of test bearing

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图 14 试验轴承信号1包络谱

Figure 14. Envelope spectrum of signal 1 of test bearing

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图 15 试验轴承原始信号2

Figure 15. Original signal 2 of test bearing

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图 16 原始试验轴承信号2包络谱

Figure 16. Envelope spectrum of signal 2 of test bearing

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图 17 SSA-IWT优化后信号2

Figure 17. Signal 2 optimized by SSA-IWT

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图 18 试验轴承信号2 EMD分解图

Figure 18. EMD diagram of signal 2 of test bearing

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图 19 SSA-IWT-EMD试验轴承信号2包络谱

Figure 19. Envelope spectrum of signal 2 of bearing in SSA-IWT-EMD test

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图 20 峭度指标试验轴承信号2包络谱

Figure 20. Envelope spectrum of signal 2 of bearing in kurtosis indicator test

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图 21 文献[23]方法滤波后的实验轴承信号2

Figure 21. Signal 2 of test bearing filtered by literature [23] method

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图 22 文献[23]方法EMD分解图

Figure 22. EMD diagram of literature [23] method

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图 23 文献[23]方法实际轴承信号2包络谱

Figure 23. Envelope spectrum of signal 2 of actual bearing by literature [23] method

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表 1 仿真信号EMD分解分量参数

Table 1. Parameters of EMD components of simulation signal

分量综合指标P 分量综合指标P 分量综合指标P

IMF1 15.517 IMF4 4.091 IMF7 2.011

IMF2 7.913 IMF5 4.798 IMF8 2.067

IMF3 9.848 IMF6 1.888 IMF9 2.307

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表 2 ER-6k轴承参数

Table 2. Parameters of ER-6k bearing

轴承节径/mm 滚珠直径/mm 滚动体个数/个故障直径/mm

38.70 8 9 1.2

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表 3 试验轴承信号1 EMD分解分量参数

Table 3. Parameters of EMD components of signal 1 of test bearing

分量综合指标P 分量综合指标P

IMF1 3.410 IMF6 2.981

IMF2 3.614 IMF7 1.431

IMF3 4.319 IMF8 1.643

IMF4 2.644 IMF9 1.564

IMF5 2.245 IMF10 0.941

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表 4 试验轴承信号2 EMD分解分量参数

Table 4. Parameters of EMD components of signal 2 of test bearing

分量综合指标P 分量综合指标P 分量综合指标P

IMF1 30.614 IMF4 4.906 IMF7 2.534

IMF2 21.787 IMF5 5.401 IMF8 2.407

IMF3 4.825 IMF6 3.149 IMF9 1.557

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表 5 试验轴承信号2EMD分解分量峭度参数

Table 5. Kurtosis parameters of EMD components of signal 2 of test bearing

分量峭度分量峭度分量峭度

IMF1 16.225 IMF4 16.772 IMF7 4.629

IMF2 14.114 IMF5 4.781 IMF8 2.686

IMF3 10.170 IMF6 5.954 IMF9 1.979

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表 6 EMD分解分量峭度参数^[23]

Table 6. Kurtosis parameters of EMD components^[23]

分量峭度分量峭度

IMF1 18.152 IMF6 2.616

IMF2 7.099 IMF7 2.479

IMF3 6.743 IMF8 1.649

IMF4 3.680 IMF9 2.202

IMF5 2.808 IMF10 1.840

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