基于FST-ATTNet的桩式可调风机基础监测数据修复研究

魏焕卫; 赵继璋; 郑晓; 谭芳; 刘聪

doi:10.12454/j.jsuese.202400966

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基于FST-ATTNet的桩式可调风机基础监测数据修复研究

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- 基于FST-ATTNet的桩式可调风机基础监测数据修复研究
- Research on Data Repair of Pile-Type Adjustable Wind Turbine Foundation Monitoring Based on FST-ATTNet
- 工程科学与技术 2025年页码：1-13
- 作者机构：
  
  1.山东建筑大学土木工程学院，山东济南 250101
  2.山东建筑大学建筑结构加固改造与地下空间工程教育部重点实验室，山东济南 250101
  3.山东建筑大学地铁保护研究所，山东济南 250101
- 作者简介：
  
  魏焕卫（1974—），男，教授，博士.研究方向：岩土工程与既有风机基础升级改造再利用. E-mail： 13181718169@163.com
  刘聪（1990—），男，副研究员，博士.研究方向：海洋岩土与桩基础工程. E-mail： 18766120289@163.com
- 基金信息：
  
  国家自然科学基金项目(12172085);山东省自然科学基金项目(ZR2019MEE021;ZR2024QE403)
- DOI：10.12454/j.jsuese.202400966
  中图分类号： TU476;TP183
- 收稿日期：2024-11-19，
  
  修回日期：2025-04-21，
  
  网络出版日期：2025-05-20，
- 稿件说明：
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魏焕卫,赵继璋,郑晓等.基于FST-ATTNet的桩式可调风机基础监测数据修复研究[J].工程科学与技术,

WEI Huanwei,ZHAO Jizhang,ZHENG Xiao,et al.Research on Data Repair of Pile-Type Adjustable Wind Turbine Foundation Monitoring Based on FST-ATTNet[J].Advanced Engineering Sciences,XXXX,XX(XX):1‒13.
魏焕卫,赵继璋,郑晓等.基于FST-ATTNet的桩式可调风机基础监测数据修复研究[J].工程科学与技术, DOI：10.12454/j.jsuese.202400966.

WEI Huanwei,ZHAO Jizhang,ZHENG Xiao,et al.Research on Data Repair of Pile-Type Adjustable Wind Turbine Foundation Monitoring Based on FST-ATTNet[J].Advanced Engineering Sciences,XXXX,XX(XX):1‒13. DOI： 10.12454/j.jsuese.202400966.

摘要

在复杂的监测系统中，数据缺失显著影响分析结果的准确性。为了解决风机可调基础监测数据中的连续缺失问题，本文提出了一种基于频域、时域和空间信息深度融合的Frequency-Spatial-Temporal Attention Network （FST-ATTNet），旨在提升复杂时间序列数据的建模能力并解决监测数据连续缺失的问题。该模型通过有机融合多种注意力机制，分别在频域、时域和空间领域进行特征提取与增强，从而显著提升时间序列模式的捕捉和预测能力，有效修复监测缺失数据。并基于实测数据进行了验证分析，得出以下结论：（1）与LSTM、GRU、BiGRU、TCN和Transformer等传统序列模型相比，FST-ATTNet在数据修复实验中表现最佳，显著优于其他模型。（2）在缺失量测试中，即使在40%的数据监测缺失情况下，FST-ATTNet依然能够保持较高修复精度；在特征缺失测试中，在25%数据缺失情况下，面对全部缺失特征仍有较高的修复精度。（3）通过消融实验，证明了FST-ATTNet结合频域、时域、空间信息和注意力机制，能够显著提升模型在高缺失数据下的修复精度。（4）FST-ATTNet不仅在桩式可调风机基础监测数据修复任务中表现出色，在另一风机基础锚索监测数据缺失率高达50%的情况下，仍展示出优异的效果。该方法可作为风机基础健康监测数据修复的可靠方法，同时为类似问题提供了新的解决思路。

Abstract

Objective

Wind energy plays a crucial role in achieving sustainable energy goals. As a critical structural component

the wind turbine foundation significantly influences the operational stability

safety

and long-term performance of wind turbine systems. However

structural health monitoring (SHM) of wind turbine foundations often faces challenges with data integrity due to environmental factors

sensor malfunctions

or data transmission issues. These missing data can severely impact the accuracy of structural health assessments

thereby affecting maintenance decisions and operational safety. To tackle the persistent data gaps in the monitoring system of adjustable wind turbine foundations

this study introduces a frequency-space-time domain attention network (FST-ATTNet). This model is designed to enhance the modeling capabilities for complex time-series data

improve the accuracy of missing data reconstruction

and ensure the reliability of health monitoring

ultimately guaranteeing wind turbines' safe and efficient operation. Moreover

it presents potential solutions for similar data reconstruction challenges across various engineering disciplines.

Methods

The FST-ATTNet model introduces an innovative data repair framework by integrating features from the frequency

time

and spatial domains. In the frequency domain

the model employs discrete cosine transform (DCT) to extract periodic and global patterns from time series data

effectively mitigating the high-frequency noise caused by the Gibbs phenomenon in traditional discrete Fourier transform (DFT). A frequency-domain attention mechanism is introduced to enhance this process

adaptively assigning weights to frequency components and prioritizing those most relevant for data reconstruction. In the time domain

Bidirectional Gated Recurrent Units (BiGRU) capture both forward and backward dependencies within the time series

ensuring a comprehensive understanding of local sequence patterns. The Kolmogorov-Arnold Network (KAN) incorporates a B-spline activation function

further enhancing the model's ability to capture complex nonlinear temporal changes. In the spatial domain

the Temporal Convolutional Network (TCN) models long-range dependencies by expanding causal convolutions

thereby capturing local and global spatial relationships. The Squeeze-and-Excitation Network (SENet) further boosts spatial feature extraction by dynamically adjusting the importance of different feature channels. By combining these various attention mechanisms

FST-ATTNet successfully integrates frequency

time

and spatial domain features

achieving superior modeling of complex time series patterns and robust reconstruction of missing data. The model is validated using monitoring data of the measured strain on a pile-based adjustable wind turbine foundation

and its performance is evaluated using the coefficient of determination (

²) and mean squared error (

MSE

) metrics.Results and Discussions　Validation experiments based on measured data show that FST-ATTNet has the following advantages: (1) Superior performance compared to traditional models: FST-ATTNet outperforms traditional sequence models in data reconstruction tasks

including Long Short-Term Memory (LSTM)

Gated Recurrent Unit (GRU)

Bidirectional GRU (Bi

GRU)

Temporal Convolutional Network (TCN)

and Transformer

achieving excellent results with

² of 0.98 and

MSE

of 0.66. In contrast

the

² of LSTM and GRU models is only 0.86

and the

MSE

is 2.35 and 2.34

respectively

which are limited by their unidirectional or local feature extraction capabilities. The Transformer model performs the worst

with

² of 0.72 and

MSE

as high as 2.77

likely due to its inability to effectively capture local patterns and frequency domain features in multivariate time series data. FST-ATTNet

through deep integration of frequency

time

and spatial information

can capture complex patterns

including periodicity

local dynamics

and long-term dependencies

significantly improving reconstruction accuracy. (2) Robustness in high missing-rate scenarios: The model excels in handling severe data-missing scenarios. In the continuous data missing test

even with a missing rate as high as 40%

FST-ATTNet maintains high reconstruction accuracy with

² of 0.94 and

MSE

of 0.95. At the 45% missing rate

performance slightly declines

with

² of 0.87 and

MSE

of 1.31

but still outperforms other models. In the feature missing test

when 25% of the monitoring features are entirely missing

the model achieves

² of 0.91 and

MSE

of 0.75

demonstrating its ability to handle complex multi-feature missing scenarios commonly found in actual monitoring systems. (3) Insights from ablation experiments: Ablation experiments provide key insights into the contribution of each component of FST-ATTNet. After removing the frequency-domain enhanced attention mechanism

² decreases to 0.92

and

MSE

increases to 1.13. After removing SENet

² is 0.91

and

MSE

is 1.22

indicating that these attention mechanisms play a crucial role in feature enh

ancement. Removing all attention mechanisms results in further performance degradation

with

² of 0.90 and

MSE

of 1.20

highlighting their importance in the prioritization of selective features. Removing the KAN network results in

² of 0.92 and

MSE

of 1.09

indicating its contribution to modeling complex time series patterns. Using only frequency domain

time domain

or spatial information results in significant performance drops

with

² of 0.75

0.68

and 0.86

respectively

and

MSE

of 1.98

2.44

and 1.33

indicating the necessity of integrating frequency

time

or spatial information. Spatial information is especially critical in high-missing scenarios. (4) Applicability of the model: To evaluate the model's applicability

FST-ATTNet was applied to anchor cable monitoring data with a missing rate of up to 50%

achieving excellent results with

² of 0.92 and

MSE

of 0.80. The model achieved near-perfect reconstruction for datasets with strong periodicity at a 25% missing rate (

² = 0.97

MSE

= 0.40). However

performance slightly declined at a 50% missing rate (

² = 0.92

MSE

= 0.80)

with deviations at the peaks primarily due to the training data not fully covering the entire cycle. Nonetheless

FST-ATTNet demonstrates adaptability across different monitoring scenarios and a unique ability to handle cyclic patterns in periodic data reconstruction.

Conclusions

The FST-ATTNet model offers a reliable and robust solution to the problem of continuous data loss in the health monitoring of pile-type adjustable wind turbine foundations. By deeply integrating frequency

time

and spatial domain information and incorporating advanced attention mechanisms

the model achieves exceptional reconstruction accuracy in high missing-rate scenarios

significantly outperforming traditional sequence models. Furthermore

the successful application of the model to other monitoring datasets (such as anchor cable data) demonstrates its versatility and broad applicability in structural health monitoring. FST-ATTNet not only enhances the reliability of wind turbine foundation monitoring but also provides innovative solutions to similar data repair challenges in other engineering domains

offering crucial support for the safety and efficiency of wind turbine systems.

关键词

Keywords

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