CMWPE结合SaE-ELM的轮对轴承故障诊断方法

张龙; 彭小明; 熊国良; 吴荣真; 胡俊锋

doi:10.13433/j.cnki.1003-8728.20200647

CMWPE结合SaE-ELM的轮对轴承故障诊断方法

doi: 10.13433/j.cnki.1003-8728.20200647

1.
华东交通大学机电与车辆工程学院, 南昌 330013
2.
中国铁路南昌局集团有限公司科学技术研究所, 南昌 330002

基金项目:

国家自然科学基金项目 51665013

国家自然科学基金项目 51865010

江西省教育厅科学技术研究项目 200616

江西省教育厅科学技术研究项目 191327

详细信息

作者简介:
张龙(1980-), 教授, 博士, 研究方向为故障诊断、智能算法, longzh@126.com

中图分类号: TH133.33;TH165⁺.3
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- 文章访问数: 84
- HTML全文浏览量: 24
- PDF下载量: 12
- 被引次数: 0
出版历程
- 收稿日期: 2021-01-14
- 刊出日期: 2023-04-25

Fault Diagnosis of Wheelset Bearings Using CMWPE and SaE-ELM

1.
School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
2.
Institute of Science and Technology, China Railway Nanchang Group Co., Ltd., Nanchang 330002, China

摘要

摘要: 针对DF4型内燃机车轮对轴承不同故障状态的判别问题, 提出了一种基于复合多尺度加权排列熵(Composit multiscale weighted permutation entropy, CMWPE)和自适应进化极限学习机(Self-adaptive evolutionary extreme learning machine, SaE-ELM)的机车轮对轴承故障识别方法。CMWPE基于复合粗粒化和加权排列熵的思想, 能很好地区分信号的不同模式。SaE-ELM通过自适应进化算法对极限学习机的输入权重、隐含层参数和输出权重进行优化, 解决了ELM随机选取网络参数的局限性, 提高了网络的泛化性能。计算机车轮对轴承不同健康状态下振动信号的CMWPE, 利用SaE-ELM识别轴承所属故障类型及故障程度。在机务段的JL-501轴承检测台上采集了7种不同健康状态的轮对轴承试件的振动信号数据。结果表明: CMWPE特征提取效果优于MPE和MWPE; SaE-ELM模式识别效果优于参数不经优化的ELM。所提方法能够有效诊断机车轮对轴承的不同故障, 且故障识别率达到100%。
- 机车轮对轴承 /
- 故障诊断 /
- 特征提取 /
- 模式识别 /
- 复合多尺度加权排列熵 /
- 自适应进化极限学习机
Abstract: Aiming at the identification of different fault states of DF4 typed diesel locomotives, a fault diagnosis method is proposed by the combination of Composite Multiscale Weighted Permutation Entropy (CMWPE) and Self-Adaptive Evolutionary Extreme Learning Machine (SaE-ELM). CMWPE is based on the idea of composite coarsening and weighted permutation entropy, which can distinguish the different modes of signals very well. SaE-ELM optimizes the input weight, hidden layer parameter and output weight of the extreme learning machine by adaptive evolutionary algorithm, which solves the limitation of ELM random selection of network parameters and improves the generalization performance of the network. The CMWPE of vibration signals of wheelset bearings in different health states is used to identify the type and degree of fault of bearings by SaE-ELM. The vibration signal data of seven wheelset bearing specimens in different health conditions were collected on the JL-501 bearing test bench of the locomotive Depot. The results show that CMWPE feature extraction is better than MPE and MWPE, and SaE-ELM pattern recognition is better than ELM without optimized parameters. The proposed method can effectively diagnose different faults of locomotive wheelset bearings, and the fault recognition rate reaches 100%.
- locomotive wheelset bearing /
- fault diagnosis /
- feature extraction /
- pattern recognition /
- CMWPE /
- SaE-ELM

HTML全文

图 1 白噪声加脉冲信号仿真图

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图 2 仿真信号的PE和WPE对比图

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图 3 具有不同长度的1/f噪声和白噪声的MPE、MWPE和CMWPE

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图 4 SaE-ELM算法流程图

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图 5 所提故障诊断方法的流程图

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图 6 轮对轴承6种故障类型实物图

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图 7 JL-501机车轴承检测台

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图 8 机车轮对轴承7种状态的时域信号

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图 9 不同m下, CMWPE分别对两种信号分析结果

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图 10 图 8中7种轮对轴承振动信号的CMWPE

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表 1 机车轮对轴承故障类型及样本数量

轴承健康状态	样本数量	状态标签
正常	80	1
外圈轻度故障	80	2
外圈中度故障	80	3
滚动体轻度故障	80	4
保持架轻度故障	80	5
保持架、滚动体复合故障	80	6
内圈轻度故障	80	7

下载: 导出CSV

表 2 不同方法的准确率对比

方法	准确率/%
CMWPE+SaE-ELM	100
CMWPE+ELM	98.57
MWPE+SaE-ELM	87.14
MPE+SaE-ELM	73.57

下载: 导出CSV

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