基于多时间尺度相似性的涡扇发动机寿命预测

Remaining useful life prediction of turbofan engine based on similarity in multiple time scales

针对传统相似性方法在提取健康指标和相似性匹配上存在的不足,提出结合自编码器神经网络的基于多时间尺度健康指标相似性的预测方法(AE MTS-HI).采用自编码器从状态监测数据中提取表征发动机退化状态的健康指标,降低提取过程非线性信息的损失.将测试退化轨迹长度的波动纳入考量,针对性地设计多时间尺度的健康指标进行相似性匹配.这不仅可以克服单一时间尺度匹配导致的精度限制,而且可以提高预测的鲁棒性.在涡扇发动机的公开数据集上验证所提方法的性能.结果表明,利用该方法能够显著提升剩余使用寿命(RUL)的预测精度,为预知维护提供有力支撑.

A novel method based on health index similarity in multiple time scales with autoencoder(AE MTS-HI)was proposed aiming at the shortage of the traditional similarity-based method in extracting health index and similarity matching.Autoencoder was applied to construct the health index based on monitoring data,which can minimize the loss of nonlinear information.The health index in multiple time scales was developed for similarity matching by considering the fluctuation of the length of test degradation trajectories.The method can remove the accuracy limitation caused by fixed time scales and enhance the prediction robustness.Performance of the proposed method was evaluated on public turbofan engines datasets.Results demonstrate that the method can improve the remaining useful life(RUL)prediction accuracy and provide stable support for predictive maintenance.