Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (12): 2827-2838.doi: 10.13229/j.cnki.jdxbgxb20210415

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

Xian-jun DU1,2,3(),Liang-liang JIA1   

  1. 1.College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China
    2.Key Laboratory of Gansu Advanced Control for Industrial Processes,Lanzhou University of Technology,Lanzhou 730050,China
    3.National Demonstration Center for Experimental Electrical and Control Engineering Education,Lanzhou University of Technology,Lanzhou 730050,China
  • Received:2021-05-10 Online:2022-12-01 Published:2022-12-08

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

Fig.1

Self-encoder network structure"

Fig.2

Schematic diagram of DAE network training"

Fig.3

Diagnostic results of SDAE network at different depths"

Fig.4

Results of 20 simulation experiments"

Fig.5

Fitness curves corresponding to different population numbers"

Fig.6

Schematic diagram of SPBO-SDAE network training"

Fig.7

Rolling bearing fault diagnosis flow chart based on SPBO-SDAE network"

Table 1

Fault types of rolling bearings in CWRU data set"

标签故障类型故障位置故障直径/cm故障程度
1Norm0正常
2B07滚动体0.017 78轻度
3B14滚动体0.035 56中度
4B21滚动体0.053 34重度
5IR07内圈0.017 78轻度
6IR14内圈0.035 56中度
7IR21内圈0.053 34重度
8OR07外圈0.017 78轻度
9OR14外圈0.035 56中度
10OR21外圈0.053 34重度

Table 2

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

SPBO编码参数符号取值
Xi1第1个隐含层节点数m146
Xi2第2个隐含层节点数m298
Xi3第3个隐含层节点数m332
Xi4第1个隐含层稀疏系数ρ10.2497
Xi5第2个隐含层稀疏系数ρ20.3216
Xi6第3个隐含层稀疏系数ρ30.1415
Xi7输入数据置零比例P0.05

Fig.8

Classification confusion matrix for bearing faults of CWRU data set"

Table 3

Fault types of rolling bearings in PU data set"

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

Table 4

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

SPBO编码参数符号取值
Xi1第1个隐含层节点数m183
Xi2第2个隐含层节点数m267
Xi3第3个隐含层节点数m342
Xi4第1个隐含层稀疏系数ρ10.2684
Xi5第2个隐含层稀疏系数ρ20.3317
Xi6第3个隐含层稀疏系数ρ30.1514
Xi7输入数据置零比例P0.04

Fig.9

Classification confusion matrix of bearing faults in PU data set"

Table 5

Fault types of rolling bearings in MFPT data set"

标签故障类型故障位置负载/kg
1baseline_1122
2baseline_1122
3baseline_1122
4OR_1外圈11
5OR_2外圈23
6OR_3外圈45
7IR_1内圈0
8IR_2内圈23
9IR_3内圈45

Table 6

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

SPBO编码参数符号取值
Xi1第1个隐含层节点数m161
Xi2第2个隐含层节点数m257
Xi3第3个隐含层节点数m331
Xi4第1个隐含层稀疏系数ρ10.2315
Xi5第2个隐含层稀疏系数ρ20.2706
Xi6第3个隐含层稀疏系数ρ30.2820
Xi7输入数据置零比例P0.07

Fig.10

Classification confusion matrix of bearing faults in MFPT data set"

Table 7

Comparison of fault diagnosis accuracy rates of different networks"

网络训练准确率/%测试准确率/%训练时间/s测试时间/s
BP91.1291.036.865.62
RBF92.3191.867.866.43
SVM85.6784.8910.474.35
SPBO-DBN93.2393.1445.378.55
3层SDAE95.9795.1423.985.77
GA-SDAE97.5497.0831.228.96
PSO-SDAE96.8996.2128.357.61
SPBO-SDAE99.8299.3024.315.93

Fig.11

Error of test set on CWRU data set"

Fig.12

Error of test set on PU data set"

Fig.13

Error of test set on MFPT data set"

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