一种滚动轴承早期微弱故障检测与诊断方法

郭盼盼; 张文斌; 崔奔; 赵春林; 尹治棚; 刘标; 刘相江

doi:10.13224/j.cnki.jasp.20230443

一种滚动轴承早期微弱故障检测与诊断方法

doi: 10.13224/j.cnki.jasp.20230443

郭盼盼^1,,
张文斌^2,*, ,,
崔奔³,
赵春林¹,
尹治棚¹,
刘标⁴,
刘相江⁵

1.
昆明理工大学机电工程学院，昆明 650500
2.
昆明学院机电工程学院，昆明 650214
3.
天津军粮城发电有限公司，天津 300300
4.
国能思达科技有限公司，北京 100089
5.
滕州市应急管理局，山东滕州 277599

基金项目: 国家自然科学基金（51769007）；云南省地方本科高校基础研究联合专项重点项目（202001BA070001-002）；兴滇英才支持计划项目经费支持（YNWR-QNBJ-2018-349）；云南省地方高校联合专项面上项目（202001BA070001-015）

详细信息

作者简介:
郭盼盼（1999-），男，硕士生，研究方向为模式识别与智能诊断。E-mail：panpan3012022@163.com

通讯作者:
张文斌（1981-），男，教授，博士，研究方向为模式识别与智能诊断。E-mail：190322507@qq.com

中图分类号: V263.6；TH13；TH17
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- 文章访问数: 925
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- 被引次数: 0
出版历程
- 收稿日期: 2023-07-07
- 网络出版日期: 2024-06-19

A method for early weak fault detection and diagnosis of rolling bearing

1.
Faculty of Mechanical and Electrical Engineering，Kunming University of Science and Technology，Kunming 650500，China
2.
School of Mechanical and Electrical Engineering，Kunming University，Kunming 650214，China
3.
Tianjin Junliangcheng Generation Company，Limited，Tianjin 300300，China
4.
CHN Energy Star Technology Company，Limited，Beijing 100089，China
5.
Tengzhou Emergency Management Bureau，Tengzhou Shandong 277599，China

摘要

摘要:
针对现有方法难以及时检测与诊断滚动轴承早期微弱故障的难题，提出一种滚动轴承早期微弱故障检测与诊断方法。基于基尼指数提取滚动轴承全寿命数据振动信号的特征指标，对轴承早期微弱故障进行及时检测；其次，基于增强奇异谱分解+蜜獾算法优化最大相关峭度解卷积的方法对轴承早期微弱故障振动信号进行有效分解，最大相关峭度解卷积降噪和凸显故障冲击效果性能，对滚动轴承早期微弱故障进行有效诊断。使用辛辛那提滚动轴承全寿命数据集进行试验，并将所提方法与传统的振动峰-峰值和有效值检测方法进行对比，该方法能够分别提前1 700 min和30 min检测并诊断出滚动轴承发生早期微弱故障。
- 滚动轴承 /
- 早期微弱故障 /
- 基尼指数 /
- 增强奇异谱分解 /
- 蜜獾算法 /
- 最大相关峭度解卷积
Abstract:
A method for detection and diagnosis of early weak faults in rolling bearings was proposed. It addressed the challenge of timely detection using existing methods. The method utilized the Gini index to extract feature indicators from the complete life cycle data of rolling bearings, enabling timely detection of early weak faults. Additionally, it applied enhanced singular spectral decomposition and the honey badger algorithm to optimize the maximum correlated kurtosis deconvolution method for effectively decomposing early weak fault signals in rolling bearings, maximizing noise reduction and highlighting fault impact, and enabling effective diagnosis of early weak faults in rolling bearings. Tests using the Cincinnati rolling bearing lifespan dataset demonstrated that the proposed method can detect and diagnose early weak faults in rolling bearings with a lead time of 1700 minutes and 30 minutes, respectively.
- rolling bearing /
- early weak fault /
- Gini index /
- enhanced singular spectral decomposition /
- honey badger algorithm /
- maximum correlation kurtosis deconvolution

HTML

图 1 正态分布的概率密度图

Figure 1. Probability density of normal distribution

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图 2 仿真信号与合成信号时域波形

Figure 2. Time domain waveforms of simulated and synthesized signals

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图 3 SSD分解结果

Figure 3. SSD decomposition results

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图 4 ESSD算法流程图

Figure 4. Flowchart of ESSD algorithm

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图 5 合成信号、仿真信号时域波形与幅值谱

Figure 5. Time domain waveform and amplitude spectrum of synthetic signal and simulated signal

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图 6 ESSD分解结果

Figure 6. ESSD decomposition results

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图 7 SSD分解结果

Figure 7. SSD decomposition results

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图 8 滚动轴承全寿命周期加速度试验台

Figure 8. Rolling bearing whole life cycle acceleration test bed

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图 9 滚动轴承状态退化趋势图

Figure 9. State degradation trend diagram of rolling bearin

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图 10 早期故障样本的包络谱

Figure 10. Envelope spectrum of early failure samples

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图 11 ESSD分解的早期故障信号

Figure 11. Early fault signals decomposed by ESSD

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图 12 ESSD分量相关系数、峭度统计图

Figure 12. Correlation coefficient and kurtosis statistics of ESSD component

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图 13 SSD分量相关系数、峭度统计图

Figure 13. Correlation coefficient and kurtosis statistics of SSD component

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图 14 EMD分量相关系数、峭度统计图

Figure 14. Correlation coefficient and kurtosis statistics of EMD component

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图 15 各最优分量包络谱结果

Figure 15. Envelope spectrum results of each optimal component

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图 16 3种寻优方式对比

Figure 16. Comparison of three optimization methods

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图 17 最优分量SSC1解卷积后的包络谱

Figure 17. Envelope spectrum of optimal component SSC1 after deconvolution

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图 18 文献[27]MCKD参数寻优方法

Figure 18. MCKD parameter optimization method in Ref. [27]

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图 19 文献[27]方法解卷积SSC1分量的包络谱

Figure 19. Envelope spectrum of the SSC1 component deconvolved by Ref.[27] method

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表 1 仿真信号能量

Table 1. Simulation signal energy

信号	y₁（t）	y₂（t）	y₃（t）	y₄（t）	y（t）
能量	0.0006238	0.4947	0.4947	0.0099	1.0000

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表 2 滚动轴承参数

Table 2. Rolling bearing parameters

型号	节径/mm	滚子直径/mm	滚子数	接触角/rad
ZA-2115	71.5	8.4	16	0.2648

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表 3 5种方法的早期故障检测结果

Table 3. Early fault detection results of five methods

指标方法	轴承早期故障起始时间/min	预警情况	实际检测故障起始时间/min
脉冲指标	5300	无	7000
峭度	5300	无	7000
峰-峰值	5300	无	7000
有效值	5300	无	5330
本文方法	5300	有	5300

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