基于时频双域融合的风电机组高速轴承故障诊断

刘杰; 李世新; 杨娜; 郭美茹

doi:10.19912/j.0254-0096.tynxb.2025-0072

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基于时频双域融合的风电机组高速轴承故障诊断

更新时间：2026-07-22

- 基于时频双域融合的风电机组高速轴承故障诊断
- Vol. 47, Issue 6, Pages: 267-279(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2025-0072
  CLC： TM614
- Online First：08 July 2026，
  
  Published：2026
- 稿件说明：
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刘杰, 李世新, 杨娜, et al. 基于时频双域融合的风电机组高速轴承故障诊断[J]. 2026, 47(6): 267-279.
DOI：

刘杰, 李世新, 杨娜, et al. 基于时频双域融合的风电机组高速轴承故障诊断[J]. 2026, 47(6): 267-279. DOI： 10.19912/j.0254-0096.tynxb.2025-0072.

摘要

针对旋转机械设备故障诊断中特征提取不充分、特征融合机制欠缺以及复杂工况下模型诊断能力不足等问题

提出一种基于双域特征融合的FFT-CNN-Informer故障诊断方法。该方法构建了并行的时域-频域特征提取架构

利用快速傅里叶变换（FFT）获取频域特征信息

设计多尺度卷积神经网络（CNN）提取局部时域特征

并引入概率稀疏自注意力机制捕获长程依赖关系

实现了对振动信号的多维度特征提取。同时

引入残差学习和自适应特征融合机制

增强了模型对不同工况下故障特征的提取能力。在凯斯西储大学轴承数据集上的实验结果表明了该方法的优越性。在风电机组轴承数据集上进行实验

诊断准确率相较于基础的Transformer、LSTM模型分别提高了6.73和9.77个百分点

消融实验进一步证实了双域特征提取架构和自适应特征融合机制的必要性

验证了该方法在实际风电场应用中的有效性和鲁棒性。

Abstract

To address the challenges of insufficient feature extraction

inadequate feature fusion mechanisms

and limited diagnostic capability under complex operating conditions in rotating machinery fault diagnosis

a novel FFT-CNN-Informer fault diagnosis method based on dual-domain feature fusion is proposed. A parallel time-frequency domain feature extraction architecture is constructed. Specifically

Fast Fourier Transform （FFT） is employed to extract frequency-domain features

a multi-scale Convolutional Neural Network （CNN） is designed to capture local temporal features

and a Probabilistic Sparse Self-Attention mechanism is introduced to capture long-range dependencies

enabling multi-dimensional feature extraction of vibration signals. Furthermore

residual learning and an adaptive feature fusion mechanism are incorporated to enhance the ability to extract fault features under varying operating conditions. Experimental results on the Case Western Reserve University bearing dataset demonstrate the superiority of the proposed method. On wind turbine bearing datasets

the proposed method outperforms baseline Transformer and LSTM models by 6.73% and 9.77% in diagnostic accuracy

respectively. Ablation studies further validate the necessity of the dual-domain feature extraction architecture and the adaptive feature fusion mechanism

confirming the effectiveness and robustness of the method in practical wind farm applications.

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references

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