| 黄崧琳,景 博,潘晋新,焦晓璇,王生龙.基于退化阶段识别与 LSTM-fine-tune 的
氧气浓缩器多阶段退化预测[J].电子测量与仪器学报,2022,36(7):136-143 |
| 基于退化阶段识别与 LSTM-fine-tune 的
氧气浓缩器多阶段退化预测 |
| Multistage degradation prediction of oxygen concentrator basedon degradation pattern recognition and LSTM-fine-tune |
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| DOI： |
| 中文关键词: 长短周期记忆网络 模式识别 迁移学习 氧气浓缩器 |
| 英文关键词:LSTM pattern recognition transfer learning oxygen concentrator |
| 基金项目:十四五装备预研共用技术项目（50902060401）、基础研究项目群（514010504 304）资助 |
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| 中文摘要: |
| 退化预测是装备健康管理的重要技术途径,近年来,大量时间序列预测方法在退化预测中得到应用。 然而,众多大型装
备由于结构复杂,功能多样,在退化过程中存在明显的阶段性,采用单一的模型对不同阶段的退化进行预测将会出现明显的精
度降低,针对不同阶段对模型重新训练也会带来时间和算力的损失。 针对多阶段退化的问题,引入了迁移学习的思想,提出了
一种退化阶段识别与 LSTM-fine-tune 结合的多阶段退化预测方法,采用退化数据对 LSTM 模型训练,之后对部分网络参数进行
冻结,在识别到装备出现新的退化阶段后,利用新阶段的退化数据对模型进行微调,以快速匹配不同阶段的数据。 为验证模型
的有效性,本文以氧气浓缩器为例进行模型应用。 结果表明,本文方法能够有效识别氧气浓缩器 3 个阶段的退化,每个阶段的
预测均方差分别为 0. 507、8. 976 和 0. 375,远低于不分段直接预测的均方误差 76. 87,在训练时间上,对比于每个阶段重新训练
时间大幅缩短,在训练精度上,明显优于维纳过程、Lstar 等传统方法。 |
| 英文摘要: |
| Degradation prediction is an important technical approach for equipment health management. In recent years, a large number
of time series prediction methods have been applied in degradation prediction. However, due to the complex structure and diverse
functions of many large equipment, there are obvious stages in the degradation process, and the application of a single model to predict
the degradation at different stages will significantly reduce the accuracy, and the retraining of the model for different stages will also bring
the loss of time and computing power. To solve the problem of multi-stage degradation, this paper introduced the idea of transfer learning
and proposed a multi-stage degradation prediction method combining degradation pattern recognition and LSTM-fine-tune. The LSTM
model was trained with degradation data, and then part of network parameters was frozen. After identifying the new degradation stage of
equipment, the model is fine-tuned with the degraded data of the new stage to quickly match the data of different stages. In order to
verify the validity of the model, this paper takes oxygen concentrator as an example to apply the model. The results show that the
proposed method can effectively identify the degradation of oxygen concentrator at three stages, and the mean square error of prediction
for each stage is 0. 507, 8. 976 and 0. 375 respectively, which is far lower than the mean square error of direct prediction without
segmentation of 76. 87. In terms of training time, compared with the retraining time of each stage, the training accuracy is obviously
superior to the traditional methods such as Wiener process and Lstar. |
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