基于MTF-MSMCNN的小样本滚动轴承故障诊断方法

段晓燕; 焦孟萱; 雷春丽; 李建华

doi:10.13224/j.cnki.jasp.20230517

基于MTF-MSMCNN的小样本滚动轴承故障诊断方法

doi: 10.13224/j.cnki.jasp.20230517

1.
兰州理工大学电气工程与信息工程学院，兰州 730050
2.
兰州理工大学机电工程学院，兰州 730050

基金项目: 国家自然科学基金（51465035）；甘肃省自然科学基金（20JR5RA466）；兰州理工大学红柳一流学科建设项目

详细信息

作者简介:
段晓燕（1981-），女，讲师，硕士，主要从事智能故障诊断研究

通讯作者:
李建华（1975-），男，教授、博士生导师，博士，主要从事智能信号处理研究。E-mail：li_jh@vip.sina.com

中图分类号: V263.6；TH133.33
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- 被引次数: 0
出版历程
- 收稿日期: 2023-08-07
- 网络出版日期: 2023-10-17

A rolling bearing fault diagnosis method based on MTF-MSMCNN with small sample

1.
School of Electrical and Information Engineering，Lanzhou University of Technology，Lanzhou 730050，China
2.
School of Mechanical and Electronical Engineering，Lanzhou University of Technology，Lanzhou 730050，China

摘要

摘要:
针对样本数量不足以及工况条件复杂导致故障识别精度低下的问题，提出一种基于马尔科夫转移场与多维监督卷积神经网络（Markov transition field and multidimensional supervised module convolutional neural networks, MTF-MSMCNN）的小样本滚动轴承故障诊断方法。采用MTF编码方式将一维滚动轴承信号转化为二维特征图像，使其保留时间相关性；提出多维监督模块（Multidimensional supervision module, MSM），在空间维度和通道维度监测重要故障特征并自适应赋予权重，提升模型捕捉关键特征的能力；将MSM嵌入到卷积神经网络中，构建出MSMCNN模型；通过试验构建复杂工况条件，将MTF图像输入到所提模型进行故障诊断，并运用两种数据集验证模型有效性。试验结果表明，MTF-MSMCNN在每类故障训练集样本仅有10个且在0 dB噪声污染下故障诊断精度依然可达90%左右，对比其他诊断模型，本文所提方法在小样本、变工况以及噪声干扰条件下具有更高的识别准确率、更强的泛化能力以及抗噪性能。
- 马尔科夫转移场 /
- 多维监督模块 /
- 滚动轴承 /
- 小样本 /
- 故障诊断
Abstract:
Considering the problem of low fault identification accuracy caused by insufficient sample size and complex working conditions, a fault diagnosis method of rolling bearing with small sample based on Markov transition field and multidimensional supervised module convolutional neural network (MTF-MSMCNN) was proposed. The one-dimensional rolling bearing signal was transformed into two-dimensional feature image using MTF coding method to preserve temporal correlation. Multidimensional supervision module (MSM) was presented to monitor important fault features in both spatial and channel dimension and assign weight adaptively, which can improve the model’s ability to capture key features. MSM was embedded into the convolutional neural network to build a MSMCNN model. The complex working conditions were constructed through experiments, and the MTF images were input into the MTF-MSMCNN network model for fault diagnosis. Two data sets were used to verify the model validity. The experimental results showed that the MTF-MSMCNN had only 10 samples in each type of fault training set, and its fault diagnosis accuracy can still reach about 90% under 0 dB noise pollution. Compared with other diagnostic models, the method proposed had higher recognition accuracy, stronger generalization ability and anti-noise performance under the conditions of small samples, variable working conditions and noise interference.
- Markov transition field /
- multidimensional supervision module /
- rolling bearing /
- small sample /
- fault diagnosis

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图 1 MTF图像生成过程

Figure 1. MTF image generation process

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图 2 CNN结构

Figure 2. CNN structure

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图 3 多维监督模块结构

Figure 3. Structure of multidimensional supervision module

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图 4 MTF-MSMCNN故障诊断网络模型

Figure 4. MTF-MSMCNN fault diagnosis network model

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图 5 美国凯斯西储大学滚动轴承试验台

Figure 5. Rolling bearing test rig of Case Western Reserve University

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图 6 MFS滚动轴承试验台

Figure 6. MFS rolling bearing test rig

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图 7 ER-16K故障部位

Figure 7. Fault location of ER-16K

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图 8 MTF二维图像

Figure 8. Two-dimensional image of MTF

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图 9 变负载箱型图

Figure 9. Variable load box type diagram

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图 10 不同样本数量下的混淆矩阵

Figure 10. Confusion matrix under different samples

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图 11 变转速各模型分类准确率

Figure 11. Classification accuracy of models with variable speed

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图 12 不同模型抗噪雷达图

Figure 12. Anti-noise radar of different models

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表 1 数据集1

Table 1. Data set 1

工况		样本数量
		D=0 mm	D=0.18 mm			D=0.36 mm
		正常（标签0）	内圈故障（标签1）	外圈故障（标签2）	滚动体故障（标签3）	内圈故障（标签4）	外圈故障（标签5）	滚动体故障（标签6）
工况A （P=0.745 7 kW）	训练集	40	40	40	40	40	40	40
工况A （P=0.745 7 kW）	测试集	100	100	100	100	100	100	100
工况B （P=1.491 4 kW）	训练集	40	40	40	40	40	40	40
工况B （P=1.491 4 kW）	测试集	100	100	100	100	100	100	100
工况C （P=2.237 1 kW）	训练集	40	40	40	40	40	40	40
工况C （P=2.237 1 kW）	测试集	100	100	100	100	100	100	100

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表 2 数据集2

Table 2. Data set 2

工况		样本数量
		D=0 mm	D=0.6 mm			D=1.2 mm
		正常（标签0）	内圈故障（标签1）	外圈故障（标签2）	滚动体故障（标签3）	内圈故障（标签4）	外圈故障（标签5）	滚动体故障（标签6）
工况D （n=1 200 r/min）	训练集	40	40	40	40	40	40	40
工况D （n=1 200 r/min）	测试集	100	100	100	100	100	100	100
工况E （n=1 300 r/min）	训练集	40	40	40	40	40	40	40
工况E （n=1 300 r/min）	测试集	100	100	100	100	100	100	100
工况F （n=1 400 r/min）	训练集	40	40	40	40	40	40	40
工况F （n=1 400 r/min）	测试集	100	100	100	100	100	100	100

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表 3 变负载各模型分类准确率

Table 3. Classification accuracy of each model under variable load

样本数量	试验方法	变负载分类准确率/%
样本数量	试验方法	A-B	A-C	B-A	B-C	C-A	C-B	平均
10	MTF-MSMCNN	99.43	97.48	99.40	98.34	97.49	99.01	98.53
	MSCNN	98.85	96.66	98.29	97.33	97.05	98.05	97.71
	GADF-MSMCNN	75.43	61.14	73.81	62.19	60.86	66.10	66.59
	MC_CNN	98.71	95.52	98.43	96.67	96.09	97.81	97.21
	ADCNN	98.91	97.43	98.80	97.87	97.47	98.01	98.08
20	MTF-MSMCNN	99.80	98.03	99.71	98.48	98.40	99.26	98.95
	MSCNN	99.10	97.04	98.95	97.81	97.71	98.47	98.18
	GADF-MSMCNN	79.62	71.05	75.81	72.95	67.71	73.14	73.38
	MC_CNN	99.04	97.57	98.90	97.82	97.14	98.71	98.20
	ADCNN	98.95	97.98	98.86	98.36	98.19	98.48	98.47
30	MTF-MSMCNN	99.91	98.57	99.89	98.89	98.63	99.66	99.26
	MSCNN	99.33	97.52	99.29	97.90	97.86	99.05	98.49
	GADF-MSMCNN	81.53	73.33	80.00	74.76	71.52	75.62	76.13
	MC_CNN	93.33	98.10	99.21	98.33	97.86	99.14	97.66
	ADCNN	99.32	98.37	99.25	98.81	98.51	98.91	98.86
40	MTF-MSMCNN	99.96	98.91	99.94	99.17	99.15	99.92	99.51
	MSCNN	99.52	97.81	99.71	98.32	98.09	99.43	98.81
	GADF-MSMCNN	83.33	74.45	81.90	77.99	72.57	83.09	78.89
	MC_CNN	99.43	98.34	99.28	99.09	98.43	99.38	98.99
	ADCNN	99.48	98.76	99.48	98.95	99.05	99.09	99.14

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