A Fault Diagnosis Method Based on Feature Pyramid CRNN Network

doi:10.16183/j.cnki.jsjtu.2021.001

Abstract

Abstract:

Aimed at the problems that the proportion and position of different fault characteristics of equipment components under variable working conditions and variable load in the signal are not fixed, and include the multi-scale complexity of the original vibration signal in a large number of different scenarios, a convolutional recurrent neural network (CRNN) rolling bearing fault diagnosis method based on feature pyramid network (FPN) was proposed. Using the convolution neural network (CNN) framework, the convolution layer of CNN and the long and short-term memory (LSTM) layer of recurrent neural network (RNN) were connected in parallel to form a new CRNN, so as to make full use of the learning ability of CNN to spatial domain information and RNN to time domain information. The weights were shared in each layer to reduce network parameters. A novel feature map was constructed using FPN, and one-dimensional signal and two-dimensional signal formed after stacking were input to extract the feature of the signal collected by the sensor, and realize fault diagnosis. The average diagnostic accuracy of this method is 99.20%, which is at least 3.62% higher than that of the basic neural network model, indicating that this method has a high diagnostic accuracy and a strong robustness. Using the bearing data set of Case Western Reserve University, it is proved that the method has a good universality. The t-SNE method was used to visually analyze the feature learning effect of the model. The results show that different fault category features have good clustering effect.

Key words: convolutional recurrent neural network (CRNN), feature pyramid, fault diagnosis, feature visualization

CLC Number:

LIU Xiuli, XU Xiaoli. A Fault Diagnosis Method Based on Feature Pyramid CRNN Network[J]. Journal of Shanghai Jiao Tong University, 2022, 56(2): 182-190.

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参数	取值
太阳轮齿数	24
行星轮齿数	24
内齿圈齿数	72
行星轮系模数/mm	0.6
压力角/(°)	20
轴承滚子数	8

行星齿轮箱状态	训练集数量	测试集数量	标签
正常	3200	800	1
滚动体缺失	3200	800	2
齿面磨损	3200	800	3
行星齿轮与断齿	3200	800	4
行星齿轮与齿面磨损	3200	800	5

编号	网络层		结构参数	算法参数			输出			补零
编号	网络层		结构参数	核数量/个	步长	循环/次	实际		语义	补零
1	复合层1	卷积层	2×2×1@32	—	2×2	—	24×	128	22×126	是
		LSTM	—	3072	—	32	24×	128	24×128	否
	池化层		2×2	—	1×1	—	24×	128	22×126	是
2	池化层		2×2	—	2×2	—	12×	64	11×63	否
3	复合层2	卷积层	2×2×32@32	—	2×2	—	12×	64	10×62	是
		LSTM	—	768	—	32	12×	64	11×63	否
		池化层	2×2		1×1		12×	64	10×62	是
4	池化层		2×2	—	2×2	—	6×	32	10×62	否
5	全连接层		—	1024	—	—	1024×	1	—	否
6	分类器		—	4	—	—	4×	1	—	否

组号	数据	ε/%
组号	数据	K=1	K=2	K=3	K=4	K=5
1	训练	99.78	99.69	99.44	98.71	99.37
	测试	98.53	99.01	97.79	99.57	98.62
2	训练	100	99.47	99.79	99.19	99.01
	测试	98.93	98.97	98.88	99.65	98.98
3	训练	99.43	99.72	98.83	99.55	99.57
	测试	99.59	97.45	100	99.30	99.21

轴承状态	训练集数量	测试集数量	标签
正常	800	200	6
轴承内圈故障	800	200	7
滚动体故障	800	200	8
轴承外圈中心方向	800	200	9
轴承外圈正交方向	800	200	10