Chinese Journal of Ship Research, Volume. 20, Issue 2, 107(2025)
A rolling bearing life prediction method based on multi-task gated networks
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Table 1. Hyperparameter settings of multitasking model
View tableView in ArticleTable 1. Hyperparameter settings of multitasking model
序号 类型 参数 1 初始学习率 0.01 2 优化器 Adam 3 输入层 (None,1) 4 BiGRU1 (None,16) 5 BiGRU2 (None,32) 6 编码器的线性层1 (None,64) 7 编码器的线性层2 (None,32) 8 编码器的线性层3 (None,20) 9 采样层的均值和方差 (None,10) 10 解码器的线性层1 (None,128) 11 解码器的线性层2 (None,32) 12 MMoE的8个专家层 (None,4) 13 HS评估的线性层1 (None,32) 14 HS评估的线性层2 (None,16) 15 HS评估的线性层3 (None,2) 16 RUL预测的线性层1 (None,32) 17 RUL预测的线性层2 (None,16) 18 RUL预测的线性层3 (None,1)
Table 2. Description of the XJTU-SY bearing dataset
View tableView in ArticleTable 2. Description of the XJTU-SY bearing dataset
数据组 运行工况 轴承1_1,轴承1_2,轴承1_3,轴承1_4,轴承1_5 2 100 r/min,12 kN 轴承2_1,轴承2_2,轴承2_3,轴承2_4,轴承2_5 2 250 r/min,11 kN 轴承3_1,轴承3_2,轴承3_3,轴承3_4,轴承3_5 2 400 r/min,10 kN
Table 3. Time-domain statistical features
View tableView in ArticleTable 3. Time-domain statistical features
序号 统计特征 公 式 1 平均值 $\bar x = \dfrac{1}{N}\displaystyle\sum\limits_{n = 1}^N {x(n)} $ 2 方差 $ \delta = \dfrac{1}{N}\displaystyle\sum\limits_{n - 1}^N {{{[x(n) - \bar x]}^2}} $ 3 绝对平均值 $|\bar x| = \dfrac{1}{N}\displaystyle\sum\limits_{n = 1}^N {|{x_n}|} $ 4 标准差 ${\sigma _x} = \sqrt {\dfrac{1}{N}\displaystyle\sum\limits_{n = 1}^N {{{[x(n) - \bar x]}^2}} } $ 5 平方根幅值 ${x_{\text{f}}} = {\left(\dfrac{1}{N}\displaystyle\sum\limits_{n = 1}^N {\sqrt {|x(n)|} } \right)^2}$ 6 均方值 ${x^2}_{{\text{rms}}} = \dfrac{1}{N}\displaystyle\sum\limits_{n = 1}^N {{x^2}(n)} $ 7 均方根值 $ {x_{{\text{rms}}}} = \sqrt {\dfrac{1}{N}\displaystyle\sum\limits_{n = 1}^N {{x^2}(n)} } $ 8 最大值 ${x_{\max }} = \max (x(n))$ 9 最小值 ${x_{\min }} = \min (x(n))$ 10 峰值 ${x_{\text{p}}} = \max |x(n)|$ 11 波形指标 $ W = {x_{{\text{rms}}}}/\left| {\bar x} \right| $ 12 峰值指标 $C = {x_{\text{p}}}/{x_{{\text{rms}}}}$ 13 脉冲指标 $I = {x_{\text{p}}}/\left| {\bar x} \right|$ 14 裕度指标 $ L = {x_{\text{p}}}/{x_{\text{f}}} $ 15 峭度指标 $S = \displaystyle\sum\limits_{n = 1}^N {{{[x(n) - \bar x]}^4}} /(N - 1)\sigma _x^4$
Table 4. MAE results of bearing dataset
View tableView in ArticleTable 4. MAE results of bearing dataset
方法 eMAE 平均 轴承1_2 轴承2_2 轴承2_5 CNN 0.112 0.152 0.054 0.106 GRU 0.046 0.073 0.052 0.057 LSTM 0.072 0.069 0.026 0.056 CNN-LSTM 0.115 0.141 0.070 0.109 SARN 0.104 0.117 0.054 0.091 多任务模型 0.021 0.026 0.016 0.021
Table 5. RMSE results of bearing dataset
View tableView in ArticleTable 5. RMSE results of bearing dataset
方法 eRMSE 平均 轴承1_2 轴承2_2 轴承2_5 CNN 0.165 0.247 0.086 0.166 GRU 0.074 0.103 0.076 0.084 LSTM 0.102 0.114 0.045 0.087 CNN-LSTM 0.173 0.215 0.088 0.159 SARN 0.171 0.173 0.093 0.146 多任务模型 0.028 0.035 0.022 0.028
Table 6. MAE results of ablation experiments for bearing dataset
View tableView in ArticleTable 6. MAE results of ablation experiments for bearing dataset
方法 eMAE 平均 轴承1_2 轴承2_2 消融模型 0.048 0.039 0.043 完整模型 0.021 0.026 0.023
Table 7. RMSE results of ablation experiments for bearing dataset
View tableView in ArticleTable 7. RMSE results of ablation experiments for bearing dataset
方法 eRMSE 平均 轴承1_2 轴承2_2 消融模型 0.074 0.050 0.062 完整模型 0.027 0.034 0.031
Table 8. MAE results for bearing dataset
View tableView in ArticleTable 8. MAE results for bearing dataset
方法 eMAE 平均 轴承1_2 轴承2_2 GRU 0.112 0.080 0.096 LSTM 0.164 0.011 0.088 多任务模型 0.041 0.028 0.035
Table 9. RMSE results FOR bearing dataset
View tableView in ArticleTable 9. RMSE results FOR bearing dataset
方法 eRMSE 平均 轴承1_2 轴承2_2 GRU 0.188 0.116 0.152 LSTM 0.184 0.121 0.153 多任务模型 0.053 0.037 0.045
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Liuyang SONG, Chuanhao ZHENG, Ye JIN, Tianjiao LIN, Changkun HAN, Huaqing WANG. A rolling bearing life prediction method based on multi-task gated networks[J]. Chinese Journal of Ship Research, 2025, 20(2): 107
Paper Information
Category: Ship Intelligent O&M, and Fault Diagnosis
Received: Jun. 3, 2024
Accepted: Dec. 25, 2024
Published Online: May. 15, 2025
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