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

热力发电 2025年54卷第11期页码：58-67

新能源发电技术 | 更新时间：2026-08-11

- 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
- 作者机构：
  
  1. 西安热工研究院有限公司
  2. 华能陇东能源有限责任公司
- 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
  中图分类号： P752;TM614
- 纸质出版：2025
- 稿件说明：
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[1]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],2025,54(11):58-67.
[1]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],2025,54(11):58-67. DOI： 10.19666/j.rlfd.202501046.

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。

Abstract

A novel healthy state monitoring method for offshore booster station platforms is proposed to enhance the damage detection capabilities under complex operating conditions. Using a deep learning framework based on memory unit autoencoders

the method magnifies fault-relevant features via denoising and angular domain resampling high-frequency vibration data from offshore boosting stations. The model employs a deep convolutional neural network to learn historical data patterns

constructs a hidden state memory bank

and achieves sparse matching between sample encoded features and the memory bank. Finally

a Gaussian mixture probability model is employed to model the generated membership scores to assess the health status of the booster station. A case study of the offshore booster station in Rudong

Jiangsu

validates the approach

achieving an anomaly recall rate and accuracy of over 98%

outperforming other comparison algorithms.

关键词

Keywords

references

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