基于子领域自适应的变工况下滚动轴承故障诊断

doi:10.13229/j.cnki.jdxbgxb20210657

Abstract

Abstract:

Aiming at the problem of inconsistent feature distribution of rolling bearing vibration data collected under variable operating conditions and difficulty in obtaining the labels of the samples to be identified， a sub-domain adaptive deep transfer learning fault diagnosis method was proposed. Firstly， to make full use of the image feature extraction capabilities of the convolutional neural network （CNN）， the rolling bearing vibration signal was used to generate an image data set using continuous wavelet transform （CWT）.Secondly， the common feature extraction of the source domain and the target domain adopted the ResNet-50 model structure of improved image set pre-training， and the sub-domain adaptive metric introduced the local maximum mean discrepancy （LMMD） criterion. This metric is used for sub-domain adaptation by calculating pseudo-labels in the target domain to match the conditional distribution distance， thereby reducing the difference in the distribution of sub-categories of faults under different working conditions and improving the accuracy of model diagnosis. Finally， experiments on two public variable-condition rolling bearing fault data sets verify that the proposed method has an average recognition accuracy of about 99%. Compared with the results of different transfer learning methods， the effectiveness and superiority of the proposed method are demonstrated.

Key words: rolling bearing, fault diagnosis, subdomain adaptation, variable working condition, residual network

CLC Number:

TH17

Shao-jiang DONG,Peng ZHU,Xue-wu PEI,Yang LI,Xiao-lin HU. Fault diagnosis of rolling bearing under variable operating conditions based on subdomain adaptation[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 288-295.

Figures/Tables 11

Fig.1

Fig.2

Table 1

Table 2

Table 3

Diagnostic accuracy of different models"

迁移任务	M1	M2	M3	M4	M5	M6
$0 → 1 ? h p$	90.70	90.75	92.40	96.85	96.00	99.85
$0 → 2 ? h p$	80.05	81.35	98.80	93.80	95.85	100.00
$0 → 3 ? h p$	80.00	89.25	91.05	88.50	94.85	99.90
$1 → 0 ? h p$	80.15	81.05	91.90	99.10	98.90	99.85
$1 → 2 ? h p$	86.15	86.80	89.75	99.35	99.45	100.00
$1 → 3 ? h p$	83.70	85.75	87.45	98.40	99.05	99.95
$2 → 0 ? h p$	81.30	83.80	90.25	96.35	95.65	99.80
$2 → 1 ? h p$	91.80	92.30	96.95	96.55	96.95	99.60
$2 → 3 ? h p$	76.25	83.00	85.30	99.70	99.70	100.00
$3 → 0 ? h p$	78.10	81.65	86.75	87.05	90.90	99.55
$3 → 1 ? h p$	81.10	85.60	88.40	95.90	95.40	99.70
$3 → 2 ? h p$	93.80	95.50	98.35	99.20	99.75	100.00
AVG	83.59	86.40	91.44	95.90	96.87	99.85

Table 3

Fig.3

Fig.4

Table 4

Bearing test bench operating instructions"

编号	转速/（r $?$ min $- 1$ ）	扭矩/（N $?$ m）	径向加载/N	标识
0	1500	0.7	1000	N15_M07_F10
1	900	0.7	1000	N09_M07_F10
2	1500	0.1	1000	N15_M01_F10
3	1500	0.7	400	N15_M07_F04

Table 4

Table 5

Transfer task description"

迁移任务

源域代码

目标域代码

健康状态

标签

E 0 → A 0, A 2, A 3

K003

KA01

KI01

K001

KA16

E 2 → A 0, A 2, A 3

E 3 → A 0, A 2, A 3

Table 5

Table 6

Fig.5

References 15

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故障类型	尺寸/英寸	样本总数	标签	描述
滚动体故障	0.007	200	0	BF07
	0.014	200	1	BF14
	0.021	200	2	BF21
内圈故障	0.007	200	3	IF07
	0.014	200	4	IF14
	0.021	200	5	IF21
正常	-	200	6	NO
外圈故障	0.007	200	7	OF07
	0.014	200	8	OF14
	0.021	200	9	OF21

层名	激活函数	参数结构
输入层		3×224×224
卷积1+批归一化层（BN）	ReLU	16×7×7
池化		步长为2
卷积2+批归一化层（BN）	ReLU	32×5×5
池化		步长为2
卷积3+批归一化层（BN）	ReLU	64×3×3
池化		步长为2
全连接层1	ReLU	125 44×2048
全连接层2	ReLU	2048×1000
全连接层3	Softmax	1000×10

迁移任务	M1	M2	M3	M4	M5	M6
A	60.61	65.72	66.67	81.22	88.28	99.61
B	62.97	65.67	66.50	80.89	99.22	99.50
C	62.94	67.06	70.78	89.83	88.44	98.11
AVG	62.17	66.15	67.98	83.98	91.98	99.07

Fault diagnosis of rolling bearing under variable operating conditions based on subdomain adaptation

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