基于优化堆叠降噪自编码器的滚动轴承故障诊断

doi:10.13229/j.cnki.jdxbgxb20210415

Abstract

Abstract:

In view of the problem that hyper parameters such as the number of hidden layers， the number of nodes in each hidden layer， the sparse coefficient and the dropout ratio of input data will directly affect the diagnosis performance of the network when deep neural network is used for rolling bearing fault diagnosis， an intelligent fault diagnosis method of rolling bearing based on optimized and improved stacked denoising auto encoders （SDAE） was proposed. The student psychology based optimization （SPBO） algorithm was used to adaptively select the hyper parameters of the denoising auto encoder （DAE） network to determine the optimal structure and parameters of SDAE network. Then the fault state features with stronger representation was extracted and input to soft-max classifier to achieve accurate diagnosis of rolling bearing operating conditions. Three open source datasets are used to verify the performance of the proposed network， the results illustrate that the diagnosis method based on SPBO-SDAE network is superior to support vector machine （SVM）， back propagation （BP） neural network， radial basis function （RBF） neural network， traditional SDAE network， SPBO based deep belief network （DBN）， genetic algorithm （GA） based SDAE network and particle swarm optimization （PSO） based SDAE network in feature extraction， diagnosis speed and fault diagnosis accuracy.

Key words: fault diagnosis, rolling bearing, stacked denoising auto encoders, hyperparametric optimization, feature extraction

CLC Number:

TP277

Xian-jun DU,Liang-liang JIA. Fault diagnosis of rolling bearing based on optimized stacked denoising auto encoders[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 2827-2838.

Figures/Tables 20

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 1

Table 2

SPBO optimizing hyper parameters of SDAE network（CWRU data set）"

SPBO编码	参数	符号	取值
$X i 1$	第1个隐含层节点数	$m 1$	46
$X i 2$	第2个隐含层节点数	$m 2$	98
$X i 3$	第3个隐含层节点数	$m 3$	32
$X i 4$	第1个隐含层稀疏系数	$ρ 1$	0.2497
$X i 5$	第2个隐含层稀疏系数	$ρ 2$	0.3216
$X i 6$	第3个隐含层稀疏系数	$ρ 3$	0.1415
$X i 7$	输入数据置零比例	$P$	0.05

Table 2

Fig.8

Table 3

Table 4

SPBO optimizing hyper parameters of SDAE network（PU data set）"

SPBO编码	参数	符号	取值
$X i 1$	第1个隐含层节点数	$m 1$	83
$X i 2$	第2个隐含层节点数	$m 2$	67
$X i 3$	第3个隐含层节点数	$m 3$	42
$X i 4$	第1个隐含层稀疏系数	$ρ 1$	0.2684
$X i 5$	第2个隐含层稀疏系数	$ρ 2$	0.3317
$X i 6$	第3个隐含层稀疏系数	$ρ 3$	0.1514
$X i 7$	输入数据置零比例	$P$	0.04

Table 4

Fig.9

Table 5

Table 6

SPBO optimizing hyper parameters of SDAE network（MFPT data set）"

SPBO编码	参数	符号	取值
$X i 1$	第1个隐含层节点数	$m 1$	61
$X i 2$	第2个隐含层节点数	$m 2$	57
$X i 3$	第3个隐含层节点数	$m 3$	31
$X i 4$	第1个隐含层稀疏系数	$ρ 1$	0.2315
$X i 5$	第2个隐含层稀疏系数	$ρ 2$	0.2706
$X i 6$	第3个隐含层稀疏系数	$ρ 3$	0.2820
$X i 7$	输入数据置零比例	$P$	0.07

Table 6

Fig.10

Table 7

Fig.11

Fig.12

Fig.13

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标签	故障类型	故障位置	故障直径/cm	故障程度
1	Norm	无	0	正常
2	B07	滚动体	0.017 78	轻度
3	B14	滚动体	0.035 56	中度
4	B21	滚动体	0.053 34	重度
5	IR07	内圈	0.017 78	轻度
6	IR14	内圈	0.035 56	中度
7	IR21	内圈	0.053 34	重度
8	OR07	外圈	0.017 78	轻度
9	OR14	外圈	0.035 56	中度
10	OR21	外圈	0.053 34	重度

标签	故障类型	故障位置	故障描述
1	K001	无	正常
2	K002	无	正常
3	K003	无	正常
4	K004	无	正常
5	KA01	外圈	人为损坏
6	KA03	外圈	人为损坏
7	KI01	内圈	人为损坏
8	KI03	内圈	人为损坏
9	KA04	外圈	真实损坏
10	KA15	外圈	真实损坏
11	KI04	内圈	真实损坏
12	KI14	内圈	真实损坏

标签	故障类型	故障位置	负载/kg
1	baseline_1	无	122
2	baseline_1	无	122
3	baseline_1	无	122
4	OR_1	外圈	11
5	OR_2	外圈	23
6	OR_3	外圈	45
7	IR_1	内圈	0
8	IR_2	内圈	23
9	IR_3	内圈	45

网络	训练准确率/%	测试准确率/%	训练时间/s	测试时间/s
BP	91.12	91.03	6.86	5.62
RBF	92.31	91.86	7.86	6.43
SVM	85.67	84.89	10.47	4.35
SPBO-DBN	93.23	93.14	45.37	8.55
3层SDAE	95.97	95.14	23.98	5.77
GA-SDAE	97.54	97.08	31.22	8.96
PSO-SDAE	96.89	96.21	28.35	7.61
SPBO-SDAE	99.82	99.30	24.31	5.93

Fault diagnosis of rolling bearing based on optimized stacked denoising auto encoders

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