Healthy state monitoring method for offshore booster station platforms based on memory unit autoencoder method

张瑞刚; 王大鹏; 雷航; 王嘉良; 郭楠; 任建强

doi:10.19666/j.rlfd.202501046

English

您当前的位置：

首页 >

文章列表页 >

Healthy state monitoring method for offshore booster station platforms based on memory unit autoencoder method

Thermal Power Generation Vol. 54, Issue 11, Pages: 58-67(2025)

更新时间：2026-08-11

- 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：
- DOI：10.19666/j.rlfd.202501046
  CLC： P752;TM614
- Published：2025
- 稿件说明：
移动端阅览
[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

ZHENG T, XIE W, YAO Z, et al. Online monitoring and analysis system for steel structure safety of offshore wind farm booster station based on multi-source data[C]. IET Conference Proceedings CP843, 2023.

AMAECHI C V, REDA A, BUTLER H O, et al. Review on fixed and floating offshore structures. Part I:types of platforms with some applications[J]. Journal of Marine Science and Engineering, 2022, 10(8):1074.

CENTENARO E, LANY M, MEYER T, et al. Structural health monitoring solutions for offshore platforms[C].Offshore Mediterranean Conference and Exhibition.OMC, 2019:1031.

PEZESHKI H, ADELI H, PAVLOU D, et al. State of the art in structural health monitoring of offshore and marine structures[C]. Proceedings of the Institution of Civil Engineers-Maritime Engineering. Thomas Telford Ltd.,2023:89-108.

LIANG T, WANG S, LU C, et al. Environmental impact evaluation of an iron and steel plant in China:normalized data and direct/indirect contribution[J]. Journal of Cleaner Production, 2020, 264:121697.

DONG Y, FRANGOPOL D M. Performance-based seismic assessment of conventional and base-isolated steel buildings including environmental impact and resilience[J]. Earthquake Engineering&Structural Dynamics, 2016, 45(5):739-756.

司广全,赵勇,童博,等.海上风电水下资产质量缺陷及检测方法[J].热力发电, 2024, 53(12):77-85.SI Guangquan, ZHAO Yong, TONG Bo, et al. Quality defects of underwater assets in offshore wind power and the detecting methods[J]. Thermal Power Generation,2024, 53(12):77-85.

张宝锋,赵勇,韩斌.海上升压站建造阶段质量控制[J].热力发电, 2019, 48(7):137-141.ZHANG Baofeng, ZHAO Yong, HAN Bin. Quality control of offshore substation construction in construction stage[J]. Thermal Power Generation, 2019, 48(7):137-141.

LIAN J, CAI O, DONG X, et al. Health monitoring and safety evaluation of the offshore wind turbine structure:a review and discussion of future development[J].Sustainability, 2019, 11(2):494.

AMBHORE N, KAMBLE D, CHINCHANIKAR S, et al.Tool condition monitoring system:a review[J]. Materials Today:Proceedings, 2015, 2(4/5):3419-3428.

HAN Y, SONG Y H. Condition monitoring techniques for electrical equipment-a literature survey[J]. IEEE Transactions on Power delivery, 2003, 18(1):4-13.

KHAN M A, AKHTAR K, AHMAD N, et al. Vibration analysis of damaged and undamaged steel structure systems:cantilever column and frame[J]. Earthquake Engineering and Engineering Vibration, 2020, 19:725-737.

VERSTRYNGE E, VAN STEEN C, VANDECRUYS E,et al. Steel corrosion damage monitoring in reinforced concrete structures with the acoustic emission technique:a review[J]. Construction and Building Materials, 2022,349:128732.

贾丹桐.基于无监督学习的海上风电支撑结构损伤识别研究[D].青岛:青岛理工大学, 2022:1.JIA Dantong. Study on the damage identification of offshore wind turbine support structure based on unsupervised learning[D]. Qingdao:Qingdao University of Technology, 2022:1.

陈韬,杨三元.海上风电基础健康监测技术[J].船舶工程, 2022, 44(增刊1):47-50.CHEN Tao, YANG Sanyuan. Offshore wind turbine foundation health monitoring technology[J]. Ship Engineering, 2022, 44(Suppl.1):47-50.

魏君.基于无线传感器网络的机械异常振动信号采集方法[J].长江信息通信, 2024, 37(7):94-96.WEI Jun. Mechanical abnormal vibration signal acquisition method based on wireless sensor network[J].Changjiang Information&Communications, 2024, 37(7):94-96.

YE H, JIANG C, ZU F, et al. Design of a structural health monitoring system and performance evaluation for a jacket offshore platform in east china sea[J]. Applied Sciences, 2022, 12(23):12021.

WANG M, INCECIK A, TIAN Z, et al. Structural health monitoring on offshore jacket platforms using a novel ensemble deep learning model[J]. Ocean Engineering,2024, 301:117510.

BAO X, FAN T, SHI C, et al. Deep learning methods for damage detection of jacket-type offshore platforms[J].Process Safety and Environmental Protection, 2021, 154:249-261.

XIN S, QI Z, YANG L, et al. Deep-learning approach based on multi-data fusion for damage recognition of marine platforms under complex loads[J]. Ocean Engineering, 2024, 303:116604.

刘占领,黄远远.基于海上风电结构安全监测系统的研究[J].船舶工程, 2024, 46(增刊1):166-171.LIU Zhanling, HUANG Yuanyuan. Research on safety monitoring system for offshore wind power structures[J].Ship Engineering, 2024, 46(Suppl.1):166-171.

王火平,陈道毅.海上导管架平台结构健康监测技术研究及应用[J].石油与天然气化工, 2023, 52(2):157-164.WANG Huoping, CHEN Daoyi. Research and application of jacket platform structure health monitoring technology in offshore[J]. Chemical Engineering of Oil and Gas,2023, 52(2):157-164.

刘亚森.基于振动信号的统计时域阻尼参数研究[D].哈尔滨:哈尔滨工业大学, 2023:1.LIU Yasen. Research on statistical time-domain damping parameters based on vibration signals[D]. Harbin:Harbin Institute of Technology, 2023:1.

FAN G, LI J, HAO H. Vibration signal denoising for structural health monitoring by residual convolutional neural networks[J]. Measurement, 2020, 157:107651.

MIAO F, ZHAO R. A new method of vibration signal denoising based on improved wavelet[J]. Journal of Low Frequency Noise, Vibration and Active Control, 2022,41(2):637-645.

LECUN Y, BOSER B, DENKER J S, et al.Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, 1(4):541-551.

LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C].Proceedings of the IEEE Conference on Computer vision and Pattern Recognition, 2015:3431-3440.

HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016:770-778.

GAO H, YUAN H, WANG Z, et al. Pixel transposed convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 42(5):1218-1227.

NIU Z, ZHONG G, YU H. A review on the attention mechanism of deep learning[J]. Neurocomputing, 2021,452:48-62.

REYNOLDS D A. Gaussian mixture models[J].Encyclopedia of Biometrics, 2009, 741:659-663.

DEMPSTER A P, LAIRD N M, RUBIN D B. Maximum likelihood from incomplete data via the EM algorithm[J].Journal of the Royal Statistical Society:Series B(methodological), 1977, 39(1):1-22.

DER MAATEN L V, HINTON G E. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008,9(11):2579-2605.

SCH?LKOPF B, PLATT J C, SHAWE-TAYLOR J,et al. Estimating the support of a high-dimensional distribution[J]. Neural Computation, 2001, 13(7):1443-1471.

LIU F T, TING K M, ZHOU Z H. Isolation forest[C].IEEE International Conference on Data Mining, 2008:413-422.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Publicity Resources

换热器内异物振动对管束磨损损伤机制研究

风轮高度阵风预报模型的验证和优化研究

NiCe_xFe

静子弯叶片对多级水蒸气轴流压缩机性能的影响研究

基于堆叠集成学习的微混燃烧室NO

Related Author

No data

Related Institution

No data

⁰