基于MTF和DenseNet的滚动轴承故障诊断方法

姜家国; 郭曼利

doi:10.13272/j.issn.1671-251x.17985

基于MTF和DenseNet的滚动轴承故障诊断方法

doi: 10.13272/j.issn.1671-251x.17985

姜家国^1,,
郭曼利²

1.
滁州职业技术学院电气工程学院, 安徽滁州　239000
2.
国网安徽省电力有限公司蚌埠供电公司, 安徽蚌埠　233000

基金项目: 安徽省教育厅高校自然科学研究基金重点项目（KJ2019A1130，KJ2019A1135）；滁州职业技术学院科技创新平台项目（YJP-2021-02）；滁州职业技术学院2019年校级科研一般项目（YJY-2019-12）。

详细信息

作者简介:
姜家国（1988—），男，安徽巢湖人，硕士，研究方向为基于深度学习的故障诊断，E-mail：jiangjgjiangjg@163.com

中图分类号: TD67
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出版历程
- 收稿日期: 2022-07-20
- 修回日期: 2022-09-07
- 网络出版日期: 2022-09-23

Fault diagnosis method of rolling bearing based on MTF and DenseNet

JIANG Jiaguo^1
,,
GUO Manli²

1.
College of Electrical Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
2.
Bengbu Power Supply Company, State Grid Anhui Electric Power Co., Ltd., Bengbu 233000, China

摘要

摘要: 基于模型和基于信号处理与分析的滚动轴承故障诊断方法存在建模困难、信号特征难以提取等问题；基于浅层机器学习的滚动轴承故障诊断方法对复杂数据的特征学习能力有限；基于深度学习的滚动轴承故障诊断方法多采用卷积神经网络，但随着网络深度加深会出现梯度弥散或消失的问题，且直接将滚动轴承振动信号转换成一维或二维图像作为网络输入会无法保留信号间的时间相关性，导致信号信息丢失。针对上述问题，提出了一种基于马尔可夫变迁场（MTF）和密集连接卷积网络（DenseNet）的滚动轴承故障诊断方法。将滚动轴承振动信号通过MTF编码后生成二维图像，保留了信号的时序信息和状态迁移信息；将二维图像作为DenseNet的输入，通过DenseNet对滚动轴承振动信号故障特征进行提取，增强了特征信息传播，使特征信息得到充分利用，进而实现故障分类识别。采用凯斯西储大学轴承数据集上的数据进行试验，结果表明，该方法能有效识别滚动轴承故障类型，故障诊断准确率达99.5%。为进一步验证该方法在电动机载荷发生变化情况下的故障诊断能力及优越性，选取灰度图、包络谱图、倒频谱图和MTF生成图4种网络输入图像分别与Inception，ResNet，DenseNet 3种网络相结合的方法进行对比试验，结果表明：不同方法的故障诊断准确率均在电动机载荷不变时高于电动机载荷变化时；MTF+DenseNet方法故障诊断准确率高于其他方法，在电动机载荷发生变化的情况下仍具有较高的故障诊断准确率，平均值为94.53%，泛化性能较好。
- 矿山机械 /
- 滚动轴承 /
- 故障诊断 /
- 马尔可夫变迁场 /
- 密集连接卷积网络
Abstract: The fault diagnosis methods of rolling bearing based on model and signal processing and analysis have the problems of modeling difficulty and signal characteristic extraction difficulty. The rolling bearing fault diagnosis method based on shallow machine learning has limited capability to learn the characteristics of complex data. The convolutional neural networks are often used in rolling bearing fault diagnosis methods based on deep learning. But with the deepening of the network, gradient dispersion or disappearance will occur. And directly converting the rolling bearing vibration signal into one-dimensional or two-dimensional images as network input will not preserve the time correlation between the signals, resulting in the loss of signal information. To solve these problems, a fault diagnosis method for rolling bearing based on Markov transition field(MTF) and densely connected convolutional networks(DenseNet) is proposed. The vibration signal of the rolling bearing is coded by MTF to generate a two-dimensional image. The time sequence information and the state transition information of the signal are preserved. The two-dimensional image is taken as the input of DenseNet, and the fault characteristics of the rolling bearing vibration signal are extracted through DenseNet. The method enhances the propagation of characteristic information, makes full use of characteristic information, and then realizes fault classification and identification. The data on the Case Western Reserve University bearing dataset is used for the test. The results show that the method can effectively identify the fault types of rolling bearings, and the accuracy of fault diagnosis is 99.5%. In order to further verify the fault diagnosis capability and superiority of this method when the motor load changes, four kinds of network input images, namely, gray-scale image, envelope spectrum image, cepstrum image and MTF generation image, are selected for comparative experiments with the method of combining three networks, namely, Inception, ResNet and DenseNet. The results show that the fault diagnosis accuracy of different methods is higher when the motor load is unchanged than when the motor load is changed. The fault diagnosis accuracy of MTF+DenseNet method is higher than that of other methods. The proposed method still has a high fault diagnosis accuracy when the motor load changes, with an average value of 94.53% and good generalization performance.
- mining machinery /
- rolling bearing /
- fault diagnosis /
- Markov transition field /
- densely connected convolutional network

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图 1 DenseNet结构

Figure 1. Structure of densely connected convolutional networks

下载: 全尺寸图片幻灯片

图 2 基于MTF和DenseNet的滚动轴承故障诊断模型结构

Figure 2. Rolling bearing fault diagnosis model based on Markov transition field and densely connected convolutional networks

下载: 全尺寸图片幻灯片

图 3 故障诊断模型准确率变化曲线

Figure 3. Variation curves of accuracy of fault diagnosis model

下载: 全尺寸图片幻灯片

图 4 滚动轴承故障分类结果

Figure 4. Classification results of rolling bearing faults

下载: 全尺寸图片幻灯片

表 1 试验数据集

Table 1. Experimental dataset

故障尺寸/mm	故障位置	故障标签	数据集A (载荷0.746 kW)		数据集B (载荷1.491 kW)		数据集C (载荷2.237 kW)
故障尺寸/mm	故障位置	故障标签	训练样本数	测试样本数	训练样本数	测试样本数	训练样本数	测试样本数
0	无	1	400	100	400	100	400	100
0.18	内圈	2	400	100	400	100	400	100
	滚动体	3	400	100	400	100	400	100
	外圈	4	400	100	400	100	400	100
0.36	内圈	5	400	100	400	100	400	100
	滚动体	6	400	100	400	100	400	100
	外圈	7	400	100	400	100	400	100
0.54	内圈	8	400	100	400	100	400	100
	滚动体	9	400	100	400	100	400	100
	外圈	10	400	100	400	100	400	100

下载: 导出CSV

表 2 不同方法故障诊断结果对比

Table 2. Comparison of fault diagnosis results of different methods

网络	输入图像	准确率/%
网络	输入图像	A→A	A→B	A→C	B→A	B→B	B→C	C→A	C→B	C→C	平均
Inception	灰度图	91.7	71.3	67.3	73.6	91.4	77.1	67.5	78.4	90.6	78.77
	包络谱图	94.9	78.6	73.5	78.6	93.3	85.4	76.1	86.2	92.6	84.36
	倒频谱图	90.3	71.7	70.2	73.7	90.0	85.8	72.3	84.4	92.3	81.19
	MTF生成图	98.3	85.1	77.5	82.9	97.3	85.7	83.5	83.2	97.1	87.84
ResNet	灰度图	94.4	76.5	74.1	78.5	93.5	86.1	74.3	82.2	93.3	83.66
	包络谱图	94.5	78.0	74.1	81.5	94.8	86.2	72.3	84.6	94.4	84.49
	倒频谱图	97.0	84.0	82.9	81.8	94.1	89.4	79.2	92.9	97.0	88.70
	MTF生成图	98.8	85.8	80.7	84.7	98.4	90.6	85.3	85.1	98.2	89.73
DenseNet	灰度图	96.8	76.5	74.1	79.5	95.7	82.5	84.0	82.0	96.4	85.28
	包络谱图	97.3	83.7	76.3	86.4	98.1	81.3	73.2	87.2	97.6	86.79
	倒频谱图	98.8	86.9	85.6	87.0	98.4	96.1	83.0	94.8	97.6	92.02
	MTF生成图	99.6	92.6	84.9	92.3	99.5	96.9	92.1	93.3	99.6	94.53

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