基于多尺度自适应注意力网络的剩余寿命预测

Remaining useful life prediction based on multi-scale adaptive attention network

剩余寿命预测是复杂设备系统智能维护决策的关键和前提。不同指标数据间的潜在关系给预测的精度带来了挑战,参数的选择也增加了模型预测的困难。采用多尺度自适应注意力网络训练方法,别从纵向和横向两个维度融合数据间的特征关系,给出分段非线性目标函数,提高了预测的精度,降低了预测的均方根误差。使用自适应机制自动选择卷积核大小,提高了网络训练的效率。通过对涡扇发动机数据集进行实证分析验证了该方法在复杂系统剩余寿命预测中的有效性。

Remaining useful life prediction is the key to and premise of intelligent maintenance decision-making of complex equipment systems.The potential relationship among different index data brings challenges to prediction accuracy,while parameter selection also increases model prediction difficulty.We use the multi-scale adaptive attention network method to fuse the feature relationship among data from vertical and horizontal dimensions,respectively,and give the piecewise nonlinear target function to improve the prediction accuracy and reduce the root mean square error.The adaptive mechanism is employed to automatically select the size of convolution kernel,improving the efficiency of network training.Empirical analysis of Company-Modular aero-propulsion system simulation data sets prove the effectiveness of this method in remaining useful life prediction of complex systems.