平衡分布自适应的变工况轴承故障迁移诊断研究

王廷轩; 刘韬; 刘应东; 王振亚

doi:10.13433/j.cnki.1003-8728.20220058

平衡分布自适应的变工况轴承故障迁移诊断研究

doi: 10.13433/j.cnki.1003-8728.20220058

昆明理工大学机电工程学院，昆明　650500

基金项目: 国家自然科学基金项目(52065030)与云南省重大科技专项计划项目(202202AC080008)

详细信息

作者简介:
王廷轩（1994−），硕士研究生，研究方向为迁移学习、设备状态监测及故障诊断，1043423651@qq.com

通讯作者:
刘韬，教授，博士，kmliutao@aliyun.com

中图分类号: TN98；TN06；TH165.3
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- 文章访问数: 28
- HTML全文浏览量: 13
- PDF下载量: 8
- 被引次数: 0
出版历程
- 收稿日期: 2021-07-05
- 刊出日期: 2023-08-31

Bearing Fault Transfer Diagnosis with Balanced Distribution Adaptation Under Variable Working Conditions

Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China

摘要

摘要: 针对机器学习方法中样本满足独立同分布导致的诊断精度低下问题，提出了平衡分布自适应(BDA)算法与K-最近邻(KNN)分类算法结合的轴承故障迁移诊断方法。提取变工况下的轴承故障信号的时域特征分别作为源域和目标域，并利用Fisher判别分析方法优选特征；基于BDA将不同工况的特征样本映射至可再生希尔伯特空间，引入最大均值差异(MMD)对变工况样本的边缘分布差异和条件分布差异进行适配；利用KNN分类器对分布适配后的样本进行迁移诊断。仿真和实验表明：本文方法在同实验平台和跨实验平台迁移上，轴承诊断的准确性和分布距离相较于其他算法优势明显。
- 轴承 /
- 平衡分布自适应 /
- 变工况 /
- Fisher判别 /
- 最大均值差异 /
- 迁移诊断
Abstract: To address the problem of low diagnostic accuracy caused by samples satisfying independent identical distribution in machine learning methods, in this paper, we propose a bearing fault transfer diagnosis method combining the balanced distribution adaptive algorithm and the K-nearest neighbor classification algorithm. Firstly, the time domain features of the bearing fault signals under variable operating conditions are extracted as the source and target domains, respectively, the Fisher discriminant analysis method is used to prefer the features. Secondly, the feature samples of different working conditions are mapped to the reproducible Hilbert space based on balanced distribution adaptive, the maximum mean discrepancy is introduced to adapt the edge distribution discrepancy and conditional distribution discrepancy of the variable working condition samples. Finally, the K-nearest neighbor classifier is used to perform migration diagnosis on the samples after the distribution adaptation. Simulation and experimental validation show that the accuracy and distribution distance of the bearing diagnosis of the proposed method are significantly advantageous compared with other algorithms on both same-experimental platform and cross-experimental platform transference.
- bearing /
- balanced distribution adaptation /
- variable operating condition /
- fisher discriminant analysis /
- maximum mean discrepancy /
- transfer diagnosis

HTML全文

图 1 平衡分布自适应故障迁移诊断流程图

Figure 1. Flowchart for balancing distribution adaptive fault transfer diagnosis

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图 2 仿真样本分布适配散点图及概率密度曲线

Figure 2. Scatter plot and probability density curve of simulated sample distribution adaptation

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图 3 轴承故障试验台

Figure 3. Bearing fault test rig

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图 4 时域特征所占权重

Figure 4. Weights of time-domain characteristics

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图 5 同实验平台迁移的可视化散点图

Figure 5. Visual scatter plot of transfer within the same experimental platform

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图 6 同实验平台迁移的MMD适配距离

Figure 6. MMD adaptation distance of transfer within the same experimental platform

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图 7 跨实验平台迁移的可视化散点图

Figure 7. Visual scatter plot of transfer across different experimental platforms

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图 8 跨实验平台迁移的MMD适配距离

Figure 8. MMD adaptation distance for transfer across different experimental platforms

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表 1 CWRU轴承样本集

Table 1. CWRU bearing sample set

类型	采样频率/kHz	转速/（r·min⁻¹）	故障直径/mm
正常故障	48	1797	0
外圈故障	48	1797	0.7112
内圈故障	48	1797	0.7112
滚子故障	48	1797	0.7112
正常故障	12	1772	0
外圈故障	12	1772	0.7112
内圈故障	12	1772	0.7112
滚子故障	12	1772	0.7112
正常故障	12	1750	0
外圈故障	12	1750	0.7112
内圈故障	12	1750	0.7112
滚子故障	12	1750	0.7112

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表 2 实验室轴承样本集

Table 2. Laboratory bearing sample set

类型	采样频率/kHz	转速/（r·min⁻¹）	故障直径/mm
正常故障	25.6	600	0
外圈故障	25.6	600	1
内圈故障	25.6	600	1
滚子故障	25.6	600	1
正常故障	25.6	800	0
外圈故障	25.6	800	1
内圈故障	25.6	800	1
滚子故障	25.6	800	1
正常故障	25.6	1200	0
外圈故障	25.6	1200	1
内圈故障	25.6	1200	1
滚子故障	25.6	1200	1

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表 3 时域特征函数表达式

Table 3. Time-domain characteristic functional expressions

时域特征	函数表达式
有效值	$ {X_{{\rm{RMS}}}} = \sqrt {\dfrac{1}{n}\displaystyle\sum\limits_{i = 1}^n {{{\left\| {x(i)} \right\|}^2}} } $
歪度	$ K = \dfrac{{ \dfrac{1} {n}\displaystyle\sum\limits_{i = 1}^n {{{\left[ {x(i) - \mu } \right]}^3}} }}{{{\sigma ^3}}} $
峭度	$ K = \dfrac{{ \dfrac{1}{n}\displaystyle\sum\limits_{i = 1}^n {{{\left[ {x(i) - \mu } \right]}^4}} }}{{{\sigma ^4}}} $
峰值	$ {X_{\max }} = \max \left[ {x(i)} \right] $
峰峰值	$ X = {X_{\max }} - {X_{\min }} $
波形因数	$ W = \dfrac{{{X_{\rm {RMS}}}}}{{\left\| {\bar x} \right\|}} $
脉冲因数	$ I = \dfrac{{{X_{\max }}}}{{{X_{\rm {RMS}}}}} $
峰值因数	$ C = \dfrac{{{X_p}}}{{{X_{\rm {RMS}}}}} $
裕度	$ L = \dfrac{{{X_p}}}{{{X_r}}} $

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表 4 同实验平台迁移的不同分布差异准确率

Table 4. Accuracy of different distribution differences in transfer within the same experimental platform %

迁移任务	300 r/min→ 800 r/min	600 r/min→ 1 200 r/min	800 r/min→ 1 200 r/min
KNN	56.67	55.08	58.24
PCA	57.02	56.98	59.64
TCA	73.83	76.62	74.54
JDA	86.23	85.98	88.96
BDA	100	99.42	100

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表 5 跨实验平台迁移的不同分布差异准确率

Table 5. Accuracy of different distribution differences in transfer across different experimental platforms %

迁移任务	1 797 r/min→ 1 200 r/min	1 772 r/min→ 1 200 r/min	1 750 r/min→ 1 200 r/min
KNN	76.67	75.08	78.24
PCA	44.54	47.03	42.59
TCA	61.73	62.88	59.25
JDA	76.62	86.17	78.61
BDA	98.57	93.46	87.73

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