基于一维CNN参数优化的压缩振动信号故障诊断

doi:10.3969/j.issn.1001-506X.2020.09.05

Abstract

Abstract:

In view of the characteristics of multi-parameter for the 1-dimensional convolutional neural network (CNN), a parameter optimization method combining orthogonal test and particle swarm optimization algorithm is proposed and applied to the fault diagnosis of compressed vibration signals. Compressive sensing theory breaks through the limitation of Nyquist sampling theorem and provides an effective way for the collection and transmission of a large number of vibration signals. Firstly, a CNN fault diagnosis model based on compressed signal is established by using the "end-to-end" feature of CNN. The orthogonal test is used to make a rough evaluation of the parameter range, and the best scheme is selected. For each parameter in the scheme, the multi-objective particle swarm optimization algorithm is used to refine it, and the optimal value of each parameter is obtained. The measured signal of gear box and bearing signal from Case Western Reserve University are selected as the research objects. The experimental results show that after optimization, the output characteristic classification of non-inferior particles is obvious, and the CNN diagnosis rate is significantly improved. The results also prove the feasibility of direct fault diagnosis for compressed signals.

Key words: convolutional neural network (CNN), compressive sensing, multi-objective particle swarm, orthogonal experiment, fault diagnosis

CLC Number:

Yunfei MA, Xisheng JIA, Huajun BAI, Chiming GUO, Shuangchuan WANG. Fault diagnosis of compressed vibration signal based on 1-dimensional CNN with optimized parameters[J]. Systems Engineering and Electronics, 2020, 42(9): 1911-1919.

Figures/Tables 16

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References 30

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状态	负载/Nm	转速/rpm	训练样本	测试样本
正常状态	0.4	800	100	20
正常状态	0.8	1 200	100	20
行星轮故障	0.4	800	100	20
行星轮故障	0.8	1 200	100	20
齿圈故障	0.4	800	100	20
齿圈故障	0.8	1 200	100	20
太阳轮故障	0.4	800	100	20
太阳轮故障	0.8	1 200	100	20

序号	卷积核个数A	卷积核大小B	批尺寸C	池化层大小D	准确率/%	时间/s
1	5	50	5	2	76.9	74.99
2	5	100	10	5	93.1	43.81
3	5	200	20	10	82.5	35.41
4	10	50	10	10	87.5	82.92
5	10	100	20	2	63.1	223.78
6	10	200	5	5	95.0	194.43
7	15	50	20	5	60.0	262.63
8	15	100	5	10	92.5	256.65
9	15	200	10	2	60.6	628.43

序号	卷积核个数A	卷积核大小B	批尺寸C	池化层大小D
T₁	252.5%(154.21)	224.4%(420.54)	264.4%(526.07)	200.6%(927.2)
T₂	245.6%(501.13)	248.7%(524.24)	241.2%(755.16)	248.1%(500.87)
T₃	213.1%(1 147.71)	238.1(858.27)	205.6%(521.82)	262.5%(374.98)
m₁	84.2%(51.40)	74.8%(140.18)	88.1%(175.36)	66.9%(309.07)
m₂	81.9%(167.04)	82.9%(174.75)	80.4%(251.72)	82.7%(166.96)
m₃	71.0%(382.57)	79.4%(286.09)	68.5%(173.94)	87.5%(124.99)
R	13.2%(331.17)	8.1%(145.91)	19.6%(77.78)	20.6%(184.08)

优化目标	文献[26]	正交试验优化	正交试验+PSO
诊断成功率/%	97.5	98.1	99.4
运行时间/s	189	184.27	192.8

数据样本	故障深度/mm	电机功率/HP	电机转速/rpm
1(N)	-	0/1/2/3	1797/1772/1750/1730
2(IR)	7	0/1/2/3	1797/1772/1750/1730
3(B)	7	0/1/2/3	1797/1772/1750/1730
4(OR)	7	0/3/1/2	1797/1730/1772/1750
5(IR)	14	0/1/2/3	1797/1772/1750/1730
6(B)	14	0/1/2/3	1797/1772/1750/1730
7(OR)	14	0/1/2/3	1797/1772/1750/1730
8(IR)	21	0/1/2/3	1797/1772/1750/1730
9(B)	21	0/1/2/3	1797/1772/1750/1730
10(OR)	21	1/3/0/2	1772/1730/1797/1750

Fault diagnosis of compressed vibration signal based on 1-dimensional CNN with optimized parameters

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