强噪声干扰下采煤机行星齿轮故障诊断方法

李勇; 张启志; 庄德玉; 邱锦波; 程刚

doi:10.13272/j.issn.1671-251x.18177

强噪声干扰下采煤机行星齿轮故障诊断方法

doi: 10.13272/j.issn.1671-251x.18177

李勇^1,,
张启志²,
庄德玉²,
邱锦波²,
程刚^1, ,

1.
中国矿业大学机电工程学院，江苏徐州　221116
2.
天地上海采掘装备科技有限公司，上海　201401

基金项目: 国家自然科学基金青年基金项目（52204178）。

详细信息

作者简介:
李勇（1992—），男，江苏连云港人，讲师，博士，主要从事机电装备健康监测研究工作，E-mail：liyong2015@cumt.edu.cn

通讯作者:
程刚（1977—），男，安徽淮北人，教授，博士，主要从事机构学和故障诊断研究工作，E-mail：chg@cumt.edu.cn。

中图分类号: TD67
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出版历程
- 收稿日期: 2024-01-01
- 修回日期: 2024-06-10
- 网络出版日期: 2024-06-27

Diagnosis method for planetary gear faults in shearer under strong noise interference

1.
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
Tiandi Shanghai Mining Equipment Technology Co., Ltd., Shanghai 201401, China

摘要

摘要: 采煤机摇臂截割部行星齿轮的健康状态直接影响截割效率。针对采煤机截割煤岩过程中受多重冲击引起的强噪声干扰、齿轮结构复杂且传递路径多变导致故障特征难以提取等特点，提出了一种基于频谱平均降噪和相关谱的采煤机行星齿轮故障诊断方法。根据信号频谱分布特征及噪声随机特性，采用频谱平均降噪方法抑制噪声对信号频谱的干扰，获得信号降噪频谱。构建相关谱以建立少样本降噪频谱和多样本降噪频谱的内在联系，减少频谱平均降噪对样本数量的需求。采用一维卷积神经网络（1D CNN）建立相关谱与故障类别之间的精确映射关系，以相关谱为输入、故障类别为输出，实现行星齿轮故障分类识别。在DDS传动系统故障诊断实验台对基于频谱平均降噪和相关谱的采煤机行星齿轮故障诊断方法进行实验验证，结果表明该方法能够增强表征故障特征的关键频率，对正常、断齿、磨损、缺齿和裂纹5种行星齿轮健康状态信号的整体识别率达96%，在信噪比不低于15 dB时可有效、准确地实现齿轮故障诊断。
- 采煤机 /
- 齿轮故障诊断 /
- 强噪声干扰 /
- 频谱平均降噪 /
- 相关谱
Abstract: The health status of the planetary gears in the cutting section of shearer's rocker arm directly affects the cutting efficiency. The strong noise interference caused by multiple impacts during the cutting of coal and rock by the shearer, the complex gear structure, and the variable transmission path make it difficult to extract fault features. In order to solve the above problems, a fault diagnosis method for planetary gears in shearer based on spectral average denoising and correlation spectrum is proposed. Based on the distribution features of signal spectrum and the random features of noise, the spectrum average denoising method is adopted to suppress the interference of noise on the signal spectrum and obtain the signal denoising spectrum. The method constructs relevant spectra to establish the intrinsic relationship between few sample denoising spectra and multi sample denoising spectra, and reduce the demand for sample size for average spectrum denoising. The method uses a one-dimensional convolutional neural network (1D CNN) to establish an accurate mapping relationship between correlation spectra and fault categories, with correlation spectra as input and fault categories as output, to achieve planetary gear fault classification and recognition. The experimental verification of the fault diagnosis method for planetary gears in shearer based on spectral average denoising and correlation spectrum is carried out on the drivetrain diagnostics simulator transmission system fault diagnosis experimental platform. The results show that the method can enhance the key frequency that characterizes the fault features. The overall recognition rate for five types of health status signals of planetary gears, including normal, broken teeth, wear, missing teeth, and cracks, reaches 96%. Gear fault diagnosis can be effectively and accurately achieved when the signal-to-noise ratio is not less than 15 dB.
- shearer /
- gear fault diagnosis /
- strong noise interference /
- spectral average noise reduction /
- correlation spectrum

HTML全文

图 1 1D CNN结构

Figure 1. Structure of one-dimensional convolutional neural network (1D CNN)

下载: 全尺寸图片幻灯片

图 2 采煤机截割部行星齿轮故障诊断流程

Figure 2. Fault diagnosis flow of planetary gear in cutting section of shearer

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图 3 DDS传动系统故障诊断实验台

Figure 3. Experimental platform for fault diagnosis of drivetrain diagnostics simulator (DDS) transmission system

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图 4 太阳轮5种健康状态信号样本实例

Figure 4. Signal samples examples of five types of sun gear health states

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图 5 不同信噪比下的断齿故障信号

Figure 5. Fault signals of broken tooth under different signal-to-noise ratio

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图 6 断齿信号频谱平均降噪结果

Figure 6. Average denoising results of frequency spectrum of broken tooth signals

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图 7 不同叠加样本数量下齿轮健康状态信号降噪效果

Figure 7. Denosing effect of gear health state signals under different superposed samples number

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图 8 测试信号与频谱参照样本集的相关谱及其可视化显示

Figure 8. Correlation spectrum of test signals and frequency spectrum reference sample sets and its visual display

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图 9 信噪比为15 dB时1D CNN训练过程和识别结果

Figure 9. 1D CNN training process and fault recognition results when signal-to-noise ratio is 15 dB

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图 10 不同信噪比和叠加样本数量下齿轮故障诊断结果

Figure 10. Fault diagnosis results of gear under different signal-to-noise ratio and superposed sample number

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表 1 降噪信号的频谱峰均差

Table 1. Difference of frequency spectrum peak value and its average value of the denosing signal

叠加样本数量/个	频谱峰值/g	频谱平均值/g	频谱峰均差/g
1	0.006 48	0.002 13	0.004 35
2	0.006 94	0.002 30	0.004 64
3	0.009 25	0.003 04	0.006 21
4	0.013 34	0.004 41	0.008 93
5	0.015 60	0.005 64	0.009 96
6	0.019 15	0.006 68	0.012 47
7	0.021 87	0.007 33	0.014 54
8	0.026 13	0.008 44	0.017 69

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