针对不同工况下多组件设备退化数据分布存在差异导致设备的寿命预测模型精度下降的问题,本文提出一种能适应于 不同工况的域特征融合网络(DFF-Net)。 首先,把不同工况的退化数据输入到特征提取网络以获取跨工况特征,然后利用域特 征融合网络(DFF-Net)对跨工况特征进行域适应调整,最后把调整后的数据输入寿命预测模型,输出不同工况下设备的寿命预 测结果。 通过在公开数据集上的试验表明,相比于没有增加域特征融合网络的寿命预测模型,本文模型在测试集上预测结果的 MAE 和 RMSE 分别降低了 6. 5%和 7. 4%,说明本文模型能有效地提高跨工况设备寿命预测的准确率。
In order to solve the problem that the accuracy of life prediction model of multi-component equipment decreases due to the difference in the distribution of degraded data under different working conditions, a domain feature fusion network (DFF-Net) which can adapt to different working conditions is proposed in this paper. Firstly, the degraded data of different working conditions were input into the feature extraction network to obtain the cross-working conditions characteristics. Then, the domain feature fusion network (DFF-Net) was used to adjust the cross-working conditions characteristics. Finally, the adjusted data was input into the life prediction model to output the life prediction results of the equipment under different working conditions. Tests on public data sets show that the MAE and RMSE of the predicted results of the proposed model on the test set decrease by 6. 5% and 7. 4%, respectively, compared with the lifetime prediction model without adding the domain feature fusion network, which indicates that the proposed model can effectively improve the accuracy of cross-working condition equipment life prediction.
黄 浩,邓耀华,唐佳敏.基于域特征融合网络的跨工况下多组件设备 寿命预测方法研究[J].电子测量与仪器学报,2023,37(5):189-197复制