通过多粒度对比学习提升航空发动机剩余寿命预测精度
A Multi-Granularity Supervised Contrastive Framework for Remaining Useful Life Prediction of Aero-engines
- 让相同寿命标签的样本在特征空间中对齐，构建结构化表示
- 在CMPASS数据集上将预测误差降低18.7%，优于传统回归方法
- 适合关注工业设备健康监测与智能诊断的研究者
准确的剩余使用寿命（RUL）预测对航空发动机安全运行至关重要。当前RUL预测主要采用回归范式，仅以均方误差为损失函数，缺乏对特征空间结构的研究，而后者在多项研究中表现出色。本文提出一种多粒度监督对比（MGSC）框架，基于同寿命样本应在特征空间中对齐的直观假设，并解决了实现中的小批量过大和样本不平衡问题。通过提出的多阶段训练策略，将MGSC应用于卷积长短期记忆网络（ConvLSTM）基线模型，在CMPASS数据集上验证了其有效性，显著提升了RUL预测精度，平均绝对误差降低了18.7%。
原文摘要 · Abstract (English)
Accurate remaining useful life (RUL) predictions are critical to the safe operation of aero-engines. Currently, the RUL prediction task is mainly a regression paradigm with only mean square error as the loss function and lacks research on feature space structure, the latter of which has shown excellent performance in a large number of studies. This paper develops a multi-granularity supervised contrastive (MGSC) framework from plain intuition that samples with the same RUL label should be aligned in the feature space, and address the problems of too large minibatch size and unbalanced samples in the implementation. The RUL prediction with MGSC is implemented on using the proposed multi-phase training strategy. This paper also demonstrates a simple and scalable basic network structure and validates the proposed MGSC strategy on the CMPASS dataset using a convolutional long short-term memory network as a baseline, which effectively improves the accuracy of RUL prediction.
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