Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (8): 2156-2166.doi: 10.13229/j.cnki.jdxbgxb.20221339

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Rolling bearing fault diagnosis based on optimized A-BiLSTM

Ping YU1,2,3(),Kang ZHAO1,Jie CAO1   

  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:2022-10-18 Online:2024-08-01 Published:2024-08-30

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Abstract:

In order to improve the efficiency of hyperparameter setting and its adaptability to the model， and break the high cost and low efficiency of manual parameter setting， a fault diagnosis method for rolling bearings based on the honey badger algorithm （HBA） optimizing bi-directional long short-term memory （BiLSTM） with attention mechanism （HBA-A-BiLSTM） is proposed. Firstly， search the optimal hyperparameter combination of the A-BiLSTM model through HBA. Secondly， the fault diagnosis performance is tested based on the A-BiLSTM model under the optimal hyperparameters. Finally， the generalization ability of the model is tested based on the datasets under different working conditions. The CWRU dataset is used to verify the fault diagnosis effect of the proposed method， which is used the diagnostic accuracy and the confusion matrix to evaluate. It is shown that， compared with other swarm intelligence optimization algorithms， the HBA has better global searching performance and faster convergence speed. The fault diagnosis accuracy of the optimized model has reached 99.5%， which has a good effect， also under different working conditions， it can achieve stable and accurate fault diagnosis performance， and has strong generalization ability.

Key words: fault diagnosis, honey badger algorithm, parameters optimization, bidirectional long short-term memory network, attention mechanism

CLC Number: 

  • TH133.3

Fig. 1

Inverse square law"

Fig. 2

Digging phase"

Fig. 3

Structure of the LSTM unit"

Fig. 4

Structure of the BiLSTM unit"

Fig. 5

Structure of Attention mechanism"

Fig. 6

Structure of A-BiLSTM"

Fig. 7

Flowchart of LSTM optimized by HBA"

Table 1

Classification of the rolling bearing datasets"

故障标签故障类别样本长度
0正常（Normal）1 000
1内圈（IR0.007）1 000
2滚动体（B0.007）1 000
3外圈（OR0.007）1 000
4内圈（IR0.014）1 000
5滚动体（B0.014）1 000
6外圈（OR0.014）1 000
7内圈（IR0.021）1 000
8滚动体（B0.021）1 000
9外圈（OR0.021）1 000

Fig. 8

Rolling bearing test system"

Table 2

The searching range of hyperparameters"

超参数最小值最大值
学习率/lr0.0010.01
训练批量大小1200
迭代次数10200
隐层1神经元个数20300
隐层2神经元个数20300
全连接层神经元个数30300

Fig. 9

Curves of fitness value"

Fig. 10

Two-dimensional visualization of HBA optimization hyperparameters"

Fig. 11

Training curve of HBA-A-BiLSTM"

Fig. 12

Result of confusion matrix"

Table 3

Searching result under different algorithms"

模型最优参数组合准确率
HBA-A-BiLSTM｛0.008，74，58，21，23，96｝99.50%
WOA-A-BiLSTM｛0.006，43，65，27，17，62｝95.45%
SSA-A-BiLSTM｛0.004，56，87，19，27，103｝94.55%

Fig. 13

Accuracy rate using different algorithms"

Table 4

Searching result under different models"

模型最优参数组合准确率
HBA-A-BiLSTM｛0.008，74，58，21，23，96｝99.50%
HBA-BiLSTM｛0.006，76，116，22，12，55｝98.29%
HBA-LSTM｛0.006，97，77，30，20，60｝96.90%

Fig. 14

Accuracy of different models"

Fig.15

Model accuracy under different conditions"

Table 5

Model performance under different conditions"

模型1 HP/%2 HP/%3 HP/%
LSTM76.1579.1558.50
BiLSTM90.2088.0067.80
A-LSTM88.3084.5568.20
A-BiLSTM97.1594.9580.10

Fig. 16

Result of confusion matrix"

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