自适应稀疏傅里叶变换在异步电机转子断条故障诊断中的应用

陈保家; 郭义鹏; 徐超; 徐罗军; 徐义; 章文忠

doi:10.7652/xjtuxb202510005

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自适应稀疏傅里叶变换在异步电机转子断条故障诊断中的应用

更新时间：2025-09-28

- 自适应稀疏傅里叶变换在异步电机转子断条故障诊断中的应用
- Application of Adaptive Sparse Fourier Transform in Diagnosis of Rotor Bar Breakage Faults in Asynchronous Motors
- 西安交通大学学报 2025年59卷第10期页码：54-63
- 作者机构：
- 作者简介：
  
  作者简介：陈保家（1977—），男，教授，博士生导师。
- 基金信息：
  
  湖北省中央引导地方科技发展基金资助项目(2024CSA095);湖北省重点研发计划资助项目(2023BAB129);湖北省自然科学基金创新发展联合基金资助项目。(2025AFD720)
- DOI：10.7652/xjtuxb202510005
  中图分类号： TM307
- 收稿：2025-04-10，
  
  纸质出版：2025-10-10
- 稿件说明：
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陈保家, 郭义鹏, 徐超, 等. 自适应稀疏傅里叶变换在异步电机转子断条故障诊断中的应用[J]. 西安交通大学学报, 2025,59(10):54-63.

CHEN Baojia, GUO Yipeng, XU Chao, et al. Application of Adaptive Sparse Fourier Transform in Diagnosis of Rotor Bar Breakage Faults in Asynchronous Motors[J]. Journal of Xi'an Jiaotong University, 2025, 59(10): 54-63.
陈保家, 郭义鹏, 徐超, 等. 自适应稀疏傅里叶变换在异步电机转子断条故障诊断中的应用[J]. 西安交通大学学报, 2025,59(10):54-63. DOI： 10.7652/xjtuxb202510005.

CHEN Baojia, GUO Yipeng, XU Chao, et al. Application of Adaptive Sparse Fourier Transform in Diagnosis of Rotor Bar Breakage Faults in Asynchronous Motors[J]. Journal of Xi'an Jiaotong University, 2025, 59(10): 54-63. DOI： 10.7652/xjtuxb202510005.

摘要

针对电机转子断条故障特征微弱且受基频干扰严重，从而导致故障特征难以提取的问题，提出了自适应稀疏傅里叶变换（ASFT）方法。首先，采用稀疏傅里叶变换（SFT）去除原始信号中的基频分量，通过改进的金雕优化算法（IGEO）实现稀疏度参数

的自适应选择；然后，对抑制基频后的信号再次应用SFT，以精确提取转子断条的故障特征分量；最后，针对高负载工况下故障分量波动大的问题，提出了基于特征重构的提取方法。为验证所提ASFT方法的有效性，将其应用于转子断条故障仿真信号及实测信号的分析中，结果表明：在常用的方法故障频率分量受基频分量显著影响时，ASFT方法提取到的故障特征分量能量占比达100%，有效解决了转子断条故障识别难度大、隐蔽性强的问题。

Abstract

To address the challenges of weak fault features and severe fundamental frequency interference in motor rotor bar breakage

which makes fault feature extraction particularly difficult

an adaptive sparse Fourier transform (ASFT) method is proposed. First

the sparse Fourier transform (SFT) is employed to remove the fundamental frequency component from the original signal

with the sparsity parameter adaptively selected using an improved golden eagle optimizer (IGEO).Subsequently

SFT is reapplied to the fundamental frequency-suppressed signal to accurately extract the rotor bar fault features. Finally

a feature-reconstruction-based extraction method is introduced to mitigate the large fluctuations of fault components under high-load conditions. To validate the effectiveness of ASFT

it is applied to both simulated and experimental signals of rotor bar breakage. The results demonstrate that when conventional methods are significantly affected by the fundamental frequency

ASFT achieves a 100% energy proportion for the extracted fault feature components

effectively resolving the issues of high diagnostic difficulty and strong concealment in rotor bar breakage faults.

关键词

Keywords

references

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