一种用于轴承故障诊断的迁移学习模型

doi:10.13229/j.cnki.jdxbgxb20190493

Abstract

Abstract:

Fault diagnosis technology can be used to detect the potential fault of the equipment by analyzing and detecting the signal, so as to ensure the operation safety and effectively improve the operation efficiency of the equipment. The bearings are widely used in rotating machinery and equipment. The fault of the bearings may seriously affect the normal operation of equipment, and inflict economic damage, even endanger the safety of staff. Therefore, it is of great theoretical and practical significance to monitor the health status of the bearing, find out the fault location and analyze its severity in time. In the actual engineering conditions, the operating environment and workspace of mechanical equipment are characterized by complexity and variability. The intelligent fault diagnosis method based on Artificial Neural Network (ANN) can effectively identify the health status of equipment, but the traditional ANN requires a large number of labeled samples for training, which greatly limits its application in equipment fault diagnosis. Also its adaptability to different working conditions is poor. In order to solve this problem, this article proposed a model of bearing fault diagnosis based on transfer learning theory. The model consists of stacked sparse AutoEncoder (SAE) and flexible maximum function (Softmax) regression. In this model, high order KL divergence (HKL) is used to train domain adaptive ability, which can transfer the working condition with a large number of known data to the similar condition with a small amount of data. Only a small amount of data is needed to train the model to adapt to the new working condition. The experimental data set of bearing from Case Western Reserve University was used to verify the effectiveness of the model.

Key words: fault diagnosis, artificial neural network, transfer learning, autoencoder

CLC Number:

TH17

Gen-bao ZHANG,Hao LI,Yan RAN,Qiu-jin LI. A transfer learning model for bearing fault diagnosis[J].Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1617-1626.

Figures/Tables 11

Table 1

Related symbols and meanings"

符号	意义	符号	意义
$X s$ 、 $X t$	源域/目标域数据集	$y s i$ 、 $y t i$	源域/目标域样本的标签
$x s$ 、 $x t$	源域/目标域样本	$N i n$ 、 $N o u t$	自动编码器输入/输出维数
$μ s n$ 、 $μ t n$	源域/目标域数据集的n阶矩	$k s$ 、 $k t$	源域/目标域样本数
$h s (i)$ 、 $h t (i)$	源域/目标域在第i层编码器的编码特征矩阵	$δ$	HKL散度的最大阶数

Table 1

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Table 2

Fig.2

Fig.3

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Fig.5

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Table 3

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工况（域）	载荷（HP）	转速/ （r·min^-1）	样本数	数据点数（每个样本）	健康状态
Ⅰ	0	1797	5000	1200	10
Ⅱ	1	1772	5000	1200	10
Ⅲ	2	1750	5000	1200	10
Ⅴ	3	1730	5000	1200	10

健康状态	方法1	方法2	方法3	方法4	方法5	方法6
平均	55.15	81.99	89.46	96.11	97.14	99.47
N	100.00	100.00	100.00	100.00	100.00	100.00
IF18	50.97	80.54	88.52	95.18	96.97	99.34
IF36	43.48	75.68	82.94	94.73	96.00	99.19
IF54	49.69	79.37	85.66	95.54	96.39	99.19
OF18	50.39	80.26	87.93	92.89	97.28	99.58
OF36	52.87	81.39	89.32	97.19	97.67	99.69
OF54	51.96	78.34	90.11	96.46	97.05	99.44
BF18	47.84	80.96	87.44	95.33	96.31	99.34
BF36	51.95	81.14	91.28	97.17	97.22	99.46
BF54	52.34	82.25	91.39	96.58	96.54	99.45

训练样本	源域样本容量	目标域样本容量	健康状态数
甲	4000	1000	10
乙	4000	750	10
丙	4000	500	10

方法	训练样本	Ⅰ-Ⅱ	Ⅰ-Ⅲ	Ⅰ-Ⅴ	Ⅱ-Ⅰ	Ⅱ-Ⅲ	Ⅱ-Ⅴ	Ⅲ-Ⅰ	Ⅲ-Ⅱ	Ⅲ-Ⅴ	Ⅴ-Ⅰ	Ⅴ-Ⅱ	Ⅴ-Ⅲ	平均
TSAE	甲	89.46	88.33	87.91	88.97	88.77	88.19	88.39	88.82	88.44	88.09	88.24	88.37	88.50
	乙	87.94	86.42	84.61	84.21	83.89	84.77	85.62	84.95	83.94	83.71	84.56	83.74	84.86
	丙	84.21	83.79	81.33	81.07	80.42	82.04	83.74	81.86	80.69	81.36	82.01	80.97	81.96
SAE+ Softmax+ KL	甲	96.11	90.94	92.44	97.38	96.51	92.68	97.91	96.99	98.00	91.36	93.98	98.74	95.25
	乙	95.83	90.61	92.14	97.05	96.18	92.30	97.66	96.72	97.08	91.19	93.59	98.66	94.92
	丙	94.69	89.11	91.09	96.52	95.44	91.00	96.92	95.53	96.07	90.76	91.99	97.48	93.88
FTNN	甲	97.14	97.49	89.16	98.00	98.64	98.28	97.51	98.41	99.31	92.94	97.99	98.18	96.92
	乙	96.94	95.18	89.02	97.88	98.21	97.99	97.23	98.19	99.26	92.83	97.88	98.10	96.56
	丙	94.29	94.68	88.33	96.68	97.64	97.69	95.90	97.73	97.88	92.42	97.39	97.66	95.69
SAE+ Softmax+ HKL	甲	99.47	99.35	99.62	99.19	98.98	98.47	99.29	98.96	99.64	99.60	98.39	99.51	99.21
	乙	99.11	99.27	99.49	98.86	98.42	98.44	99.10	98.59	99.43	99.14	97.98	99.34	98.93
	丙	98.94	99.16	99.27	97.87	97.71	97.31	98.94	97.87	98.93	98.06	97.54	98.22	98.32

A transfer learning model for bearing fault diagnosis

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