基于记忆单元自编码的海上升压站平台健康监测方法

张瑞刚; 王大鹏; 雷航; 王嘉良; 郭楠; 任建强

doi:10.19666/j.rlfd.202501046

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基于记忆单元自编码的海上升压站平台健康监测方法

新能源发电技术 | 更新时间：2025-11-21

- 基于记忆单元自编码的海上升压站平台健康监测方法
- Healthy state monitoring method for offshore booster station platforms based on memory unit autoencoder method
- 热力发电 2025年第11期页码：58-67
- 作者机构：
- 作者简介：
- 基金信息：
  
  中国华能集团有限公司总部科技项目（HNKJ24-HF72）~~
- DOI：10.19666/j.rlfd.202501046
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- 纸质出版：2025
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张瑞刚, 王大鹏, 雷航, 等. 基于记忆单元自编码的海上升压站平台健康监测方法[J]. 热力发电, 2025,(11):58-67.

张瑞刚, 王大鹏, 雷航, et al. Healthy state monitoring method for offshore booster station platforms based on memory unit autoencoder method[J]. Thermal Power Generation, 2025, (11): 58-67.
张瑞刚, 王大鹏, 雷航, 等. 基于记忆单元自编码的海上升压站平台健康监测方法[J]. 热力发电, 2025,(11):58-67. DOI： 10.19666/j.rlfd.202501046.

张瑞刚, 王大鹏, 雷航, et al. Healthy state monitoring method for offshore booster station platforms based on memory unit autoencoder method[J]. Thermal Power Generation, 2025, (11): 58-67. DOI： 10.19666/j.rlfd.202501046.

摘要

提出一种新型海上升压站平台健康监测方法，以增强其在复杂工况下的损伤检测能力。采用基于记忆单元自编码器的深度学习框架，通过对海上升压站的高频振动数据进行降噪和角域重采样来放大故障相关特征。模型利用深度卷积神经网络学习历史数据模式，构建隐藏状态记忆库，并实现样本编码特征与记忆库的稀疏匹配。最后，采用高斯混合概率模型对生成的从属分数进行建模，以评估升压站的健康状态。以江苏如东县的海上升压站为案例进行验证，实现了98%以上的异常召回率及准确率，优于其他对比算法。

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