基于并行架构网络与改进动态FD-KNN的风力发电机轴承故障预警

许伯强; 王彪; 孙丽玲; 尹彦博

doi:doi:10.19912/j.0254-0096.tynxb.2024-1121

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基于并行架构网络与改进动态FD-KNN的风力发电机轴承故障预警

更新时间：2025-12-17

- 基于并行架构网络与改进动态FD-KNN的风力发电机轴承故障预警
- Issue 10, Pages: 753-765(2025)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1121
  CLC： M315
- Published Online：31 October 2025，
  
  Published：31 October 2025
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许伯强, 王彪, 孙丽玲, et al. 基于并行架构网络与改进动态FD-KNN的风力发电机轴承故障预警[J]. 2025, (10): 753-765.
DOI：

许伯强, 王彪, 孙丽玲, et al. 基于并行架构网络与改进动态FD-KNN的风力发电机轴承故障预警[J]. 2025, (10): 753-765. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1121.

摘要

针对当前风力发电机轴承故障预警准确率和可靠性不足的问题

提出一种基于并行架构网络与改进动态k近邻故障检测（FD-KNN）的风力发电机轴承故障预警方法。首先

对风力发电机的数据采集与监控（SCADA）数据进行相关性分析

筛选出与风力发电机轴承关键变量高度相关的变量

并采用集合模态经验分解（EEMD）分解关键变量

深入挖掘关键变量内不同时间尺度的特征以及关键变量与高相关协变量的潜在相互作用。然后

构建一个结合自注意力机制的长短期记忆网络（Self-Attention-LSTM）和改进Transformer模型的新型并行架构网络

用以精确可靠地预测关键变量的未来状态。基于预测结果

计算残差

并结合风力发电机轴承的实时状态对FD-KNN算法进行动态优化

包括调整近邻规模、设置动态告警阈值和预警条件

以实现更为精准可靠的故障预警。最后

通过实际SCADA数据验证

结果表明该方法可提前识别风力发电机轴承故障

且在准确性和可靠性方面均表现出色。

Abstract

Aiming at the current problem of insufficient accuracy and reliability of wind turbine bearing fault warning

a wind turbine bearing fault warning method based on parallel architecture network with improved dynamic fault detection--nearest neighbor （FD-KNN） is proposed. Firstly

the correlation analysis of supervisory control and data acquisition （SCADA） of wind turbine is carried out to screen out the variables that are highly correlated with the key variables of wind turbine bearings

and the key variables are decomposed by using the Ensemble empirical mode decomposition （EEMD）

to dig deeply the characteristics of the key variables at different time scales and the potential interactions between key variables and highly correlated covariates. Then

a novel parallel architecture network that combines Self-Attention LSTM and an improved Transformer model is developed to accurately and reliably predict the future states of key variables. Based on the prediction results

the residuals are calculated

and the FD-KNN algorithm is dynamically optimized using the real-time state of wind turbine bearings. This optimization includes adjusting the scale of the nearest neighbors and setting dynamic warning thresholds and conditions to achieve more accurate and reliable fault warnings. Finally

the method is validated using actual SCADA data

and the results demonstrate that it can identify wind turbine bearing faults in advance

excelling in both accuracy and reliability.

关键词

Keywords

references

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