Rolling Bearing Fault Diagnosis Based on Weighted Domain Adaptive Convolutional Neural Network

doi:10.16182/j.issn1004731x.joss.22-0616

Abstract

Abstract:

A rolling bearing fault diagnosis method based on a weighted domain adaptive convolutional neural network (WDACNN) is proposed to solve the problem that the data distribution of vibration signals of rolling bearings changes due to workload changes, which leads to poor generalization of fault diagnosis algorithm. In this method, the domain adaptation algorithm is embedded in the convolutional neural network to make the classifier based on the source domain achieve excellent generalization in the target domain, and the weight coefficient is introduced to weight the samples in the source domain to reduce the influence of the class weight deviation. In the simulation experiment, six migration tasks are used to verify the effectiveness of the proposed method. The average fault diagnosis accuracy of the proposed method reaches 96.6%, which proves the effectiveness of the proposed method under different workload conditions.

Key words: bearing, fault diagnosis, transfer learning, domain adaptation, weight coefficient

CLC Number:

TP391.9

Zhang Wenfeng, Zhu Zhichao, Wu Dinghui. Rolling Bearing Fault Diagnosis Based on Weighted Domain Adaptive Convolutional Neural Network[J]. Journal of System Simulation, 2023, 35(11): 2445-2453.

Figures/Tables 8

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健康状态	标签	工作负载	测试样本
滚动体故障	0	A	320
		B	300
		C	280
内圈故障	1	A	300
		B	280
		C	260
外圈故障	2	A	300
		B	280
		C	320
正常	3	A	260
		B	320
		C	300

层数	类型	核数量	核尺寸	步长	激活函数
1	卷积层	1	3×3	2	ReLU
2	池化层	—	2×2	2	—
3	卷积层	8	3×3	1	ReLU
4	池化层	—	2×2	2	—
5	卷积层	16	3×3	1	ReLU
6	池化层	—	2×2	2	—
7	全连接层	1	800	—	Sigmoid
8	全连接层	1	100	—	Sigmoid
9	全连接层	1	20	—	Sigmoid
10	输出层	1	4	—	Softmax

诊断任务	BP	LSTM	CNN	DAN	MACNN	WDACNN
AVG	79.8	83.3	88.5	92.1	94.6	96.6
AB	85.8	87.3	92.9	96.2	97.8	99.1
AC	81.5	82.3	88.2	91.3	93.9	96.5
BA	79.6	86.1	91.3	94.1	96.2	97.1
BC	77.9	81.8	86.1	92.1	95.4	98.9
CA	77.5	79.6	84.6	88.3	91.5	93.2
CB	76.7	82.6	88.1	89.9	92.9	95.3

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