使用LSTM实现C-MAPSS数据集里面的剩余寿命预测(Pytorch)

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使用LSTM实现C-MAPSS数据集里面的剩余寿命预测(Pytorch) 每轮训练后测试集误差 score:445.4610 334.5140 358.6489 365.9250 331.4520 283.3463 460.4766 314.7196 325.5950 452.3746 RMSE:16.3614 14.8254 14.9796 15.5157 14.7853 14.2053 16.2834 14.6757 14.7481 15.8802 由实验结果可知,MS-BLSTM 的预测误差均为最低水平,并且实际训练过程中收敛速度较快,涡扇发动机接近损坏时预测准确率较高。与传统机器学习方法相比,深度学习模型如CNN 和 LSTM的预测误差相对较小。而本文所提的 MS-BLSTM 混合深度学习预测模型进一步提高了 RUL 预测精度,,这得益于 MS-BLSTM 混合模型有效利用了时间段内传感器测量值的均值和方差与RUL的相关性,并使用 BLSTM学习历史数据和未来数据的长程依赖。本文所提的 MS-BLSTM 剩余使用寿命预测模型预测精度高,可有力支撑涡扇发动机的健康管理与运维决策。

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