基于JS-VME-DBN和MS-UMAP的行星齿轮箱故障诊断方法

戚晓利; 程主梓; 崔创创; 杨艳

doi:10.13224/j.cnki.jasp.20220221

基于JS-VME-DBN和MS-UMAP的行星齿轮箱故障诊断方法

doi: 10.13224/j.cnki.jasp.20220221

安徽工业大学机械工程学院，安徽马鞍山 243032

基金项目: 安徽省自然科学基金(1808085ME152)

详细信息

作者简介:
戚晓利（1975-），男，副教授、硕士生导师，博士，主要从事故障诊断和撞击动力学等方面研究

通讯作者:
程主梓（1996-），男，硕士生，主要研究方向为机械系统状态监控和故障诊断等。E-mail：1572002078@qq.com

中图分类号: V232.8；TH165⁺.3；TN911.7
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出版历程
- 收稿日期: 2022-04-18
- 网络出版日期: 2023-06-21

Fault diagnosis method of planetary gearbox based on JS-VME-DBN and MS-UMAP

School of Mechanical Engineering， Anhui University of Technology，Ma’anshan Anhui 243032，China

摘要

摘要:
为了解决行星齿轮箱振动信号存在噪声干扰和特征提取困难的问题，提出一种基于水母搜索优化变分模态提取（JS-VME）、深度置信网络（DBN）和监督型马氏距离的均匀流形逼近与投影算法（MS-UMAP）的行星齿轮箱故障诊断方法。采集行星齿轮箱的振动信号，利用JS-VME对其进行预处理，获得相关性较强的期望IMF（intrinsic mode function）分量；然后将该IMF分量应用DBN提取特征向量，构建高维故障特征集；采用MS-UMAP进行维数约减，获得低维、敏感的故障特征；将低维故障特征集应用水母搜索优化核极限学习机（JS-KELM）判别故障类型。行星齿轮箱故障诊断实验结果表明：与UMAP、t-SNE、Isomap、LPP、W-Isomap、LLE、LTSA和MDS等方法相比，MS-UMAP算法对JS-VME-DBN的特征提取结果有着最佳的降维效果，所提方法对行星齿轮箱的裂纹、磨损和缺齿等故障的识别率达到了100%，具有一定的有效性。
- 故障诊断 /
- 行星齿轮箱 /
- 变分模态提取（VME） /
- 深度置信网络（DBN） /
- 均匀流行逼近与投影算法（UMAP） /
- 核极限学习机（KELM）
Abstract:
In order to solve the problem of the noise interference and the difficulty in feature extraction in the vibration signal of planetary gearbox, a fault diagnosis method for planetary gearboxes based on jel-lyfish search optimization variational mode extraction (JS-VME), deep belief network (DBN) and supervised Mahalanobis distance uniform manifold approximation and projection algorithms (MS-UMAP) was proposed. The vibration signals of the planetary gearbox were collected, and JS-VME was used to preprocess them to obtain expected IMF （intrinsic mode function）component with strong correlation. Then, DBN was applied to the IMF component to extract feature vectors, and the high-dimensional fault feature set was built. MS-UMAP was used for dimensionality reduc-tion to obtain low-dimensional and sensitive fault features. The low-dimensional fault feature set was applied to the jellyfish search optimization kernel extreme learning machine (JS-KELM) to de-termine fault types. The experiment results of planetary gearbox fault diagnosis showed that com-pared with UMAP, t-SNE, Isomap, LPP, W-Isomap, LLE, LTSA and MDS, the MS-UMAP algorithm had the best dimensionality reduction effect on the feature extraction results of JS-VME-DBN. The fault recognition rate of the proposed method reached 100% with a certain validity in planetary gearbox, such as the cracks, wear and missing teeth.
- fault diagnosis /
- planetary gearbox /
- variational mode extraction (VME) /
- deep belief network (DBN) /
- uniform manifold approximation and projection (UMAP) /
- kernel extreme learning machine (KELM)

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图 1 JS-VME流程图

Figure 1. Flowchart of JS-VME

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图 2 JS-VME-DBN模型

Figure 2. JS-VME-DBN model

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图 3 JS-KELM流程图

Figure 3. Flow chart of JS-KELM

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图 4 不同分类器对6种数据集识别率

Figure 4. Recognition rata of different classifiers on six data sets

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图 5 行星齿轮箱故障诊断模型

Figure 5. Fault diagnosis model of planetary gearbox

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图 6 行星齿轮箱故障诊断平台

Figure 6. Planetary gearbox fault diagnosis platform

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图 7 行星齿轮箱4种工况振动加速度信号时域波形

Figure 7. Time domain waveform of vibration acceleration signal in four working conditions of planetary gearbox

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图 8 不同分类器对测试样本的分类效果和混淆矩阵

Figure 8. Classification effect and confusion matrix of different classifiers on test samples

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图 9 9种算法降维后的三维可视化结果

Figure 9. Three-dimensional visualization results after dimensionality reduction by nine algorithms

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图 10 6种分类器对比

Figure 10. Comparisons of six classifie

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表 1 6种数据集描述

Table 1. Descriptionof six data sets

数据集	总样本数	各样本数	训练样本	测试样本
sonar_all_data	208	97, 111	20, 22	77, 89
wine	178	59, 71, 48	12, 14, 10	47, 57, 38
iris	150	50, 50, 50	10, 10, 10	40, 40, 40
diabetes	768	500, 268	100, 54	400, 214
tic_tac_toc	958	626, 332	125, 67	501, 265
splice	1000	483, 517	97, 103	386, 414

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表 2 分类器参数设置

Table 2. Classifier parameter settings

分类器	参数设置
PSO-KELM	局部搜索能力为2，全局搜索能力为2，粒子群数为10，终止迭代为100
GWO-KELM	灰狼种群规模数为10，终止迭代为100
ABC-KELM	蜂群规模数为10，初始蜜源为10，控制参数为100，最大循环次数为100
AO-KELM	金雕种群规模数为10，终止迭代为100
BA-KELM	蝙蝠种群规模设置为10，响度为0.5，脉冲重复频率为0.01，最高频率为0.5，最低频率为0.2，终止迭代次数为100
JS-KELM	水母种群规模数为10，终止迭代为100

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表 3 行星齿轮箱基本参数

Table 3. Basic parameters of planetary gearbox

级数	齿轮	齿数	级数	齿轮	齿数
1	太阳轮	20	2	太阳轮	28
	行星轮	40		行星轮	36
	内齿圈	100		内齿圈	100

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表 4 JS-VME-DBN的特征提取效果

Table 4. Feature extraction effect of JS-VME-DBN

分类方法	不同分类器对测试样本故障识别精度/%
分类方法	正常	裂纹	磨损	缺齿	平均
PSO-KELM	52.50	100	80.00	100	83.13
ABC-KELM	87.50	100	82.50	100	92.50
GWO-KELM	97.50	100	67.50	100	91.25
AO-KELM	77.50	100	100	100	94.38
BA-KELM	40.00	100	77.50	100	79.38
JS-KELM	100	100	90.00	100	97.50

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表 5 9种算法降维后的故障识别精度和性能指标

Table 5. Fault identification accuracy and performance index after dimensionality reduction of nine algorithms

降维方法	多故障分类器对测试样本故障识别精度/%					降维性能指标
降维方法	正常	裂纹	磨损	缺齿	平均	${S_{\rm{b}}}$	${S_{\rm{w}}}$	${S_{\rm{b}}}$/${S_{\rm{w}}}$
t-SNE	100	100	90.00	82.50	93.13	428.53	43.06	9.95
Isomap	85.00	100	100	100	95.00	2.33$ \times $10⁻⁴	2.29$ \times $10⁻⁵	10.17
W-Isomap	97.50	100	100	90.00	96.88	1.16$ \times $10⁻⁴	4.67$ \times $10⁻⁶	24.84
LPP	40.00	100	72.50	100	78.13	1.32$ \times $10⁻⁴	7.11$ \times $10⁻⁵	1.86
LLE	100	60.00	100	100	90.00	1.28$ \times $10⁻²	2.11$ \times $10⁻³	6.07
LTSA	100	80.00	82.50	100	90.63	6.20$ \times $10⁻³	8.79$ \times $10⁻³	0.71
MDS	100	57.50	100	100	89.38	2.31$ \times $10⁻⁴	2.29$ \times $10⁻⁵	10.10
UMAP	52.50	100	100	100	88.13	59.92	36.14	1.66
MS-UMAP	100	100	100	100	100	82.52	1.40	58.94

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