Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (2): 310-317.doi: 10.13229/j.cnki.jdxbgxb20211207

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Real⁃time detection of embedded bearing faults based on 1D⁃RSCNN

Xiu-fang WANG(),Shuang SUN,Chun-yang DING   

  1. School of Electrical Information Engineering，Northeast Petroleum University，Daqing 163318，China
  • Received:2021-11-15 Online:2022-02-01 Published:2022-02-17

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Abstract:

Aiming at the problems of traditional fault diagnosis model with many parameters， long training， long detection time， poor noise resistance and not suitable for online real-time diagnosis， the paper puts forward the rolling bearing fault diagnosis method based on residual connection and one-dimensional separable convolution（1D-RSCNN）， and constructs an embedded system consisting of Jetson Nano and signal acquisition circuit. The model dimensions are compressed with 1D separable convolution and global average pooling to improve the computational efficiency of traditional convolution， and wide convolution cores， Dropout is introduced into the residual network to improve the tolerance of noise through. The test results show that the diagnostic accuracy of this method is as high as 99.92%. Compared with other models， the diagnosis accuracy is high， the real-time is good， the anti-jamming ability is strong， and it is suitable for the real-time detection of motor bearing fault.

Key words: motor faults, embedded systems, 1D separable convolution, residual connections, Jetson Nano

CLC Number: 

  • TP277

Fig.1

1D-RSCNN model structure"

Fig.2

Residual module"

Fig.3

Improved residual module"

Fig.4

Comparison of traditional one-dimensional convolutions with one-dimensional separable convolutions"

Fig.5

A structure diagram of the full-connected and global average pool layers"

Fig.6

Bearing fault test bench"

Fig.7

Bearing failure"

Fig.8

Hardware structure and peripheral interfaces"

Table 1

Experimental dataset"

故障类型样本长度训练样本测试样本标签
滚珠故障（RU）10247001000
外圈故障（BORF）10247001001
内圈故障（BIRF）10247001002
正常（Healthy）10247001003

Fig.9

Four types of time domain signals"

Table 2

1D-RS CNN model parameters"

层结构数目×尺寸×步长输出维度参数量
输入层-1024×10
卷积层16×32×8128×16528
残差块116×1×1128×16320
32×3×1128×32592
32×3×1128×32592
残差块232×1×1128×321152
64×3×1128×642208
64×3×1128×642208
全局平均池化-64×10
Softmax层44×1260

Fig.10

Training process curve"

Fig.11

Embedded systems identify different motor faults"

Table 3

Comparison of test results for different models"

算法准确率/%模型参数训练时间/s测试时间/s
1D-RSCNN99.927 8606750.85
1D CNN99.6886 9208681.30
ResNets99.8141 9907821.06
文献［20］100.00206 1489162.54

Fig.12

Recognition accuracy in different signal-to-noise ratio scenarios"

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