| 引用本文: | 毛向德,董海鹰,梁金平.多频带多尺度模糊熵融合的牵引整流器故障诊断[J].控制理论与应用,2025,42(7):1313~1322.[点击复制] |
| MAO Xiang-de,DONG Hai-ying,LIANG Jin-ping.Traction rectifier fault diagnosis based on multi-band and multi-scale fuzzy entropy fusion[J].Control Theory & Applications,2025,42(7):1313~1322.[点击复制] |
|
| 多频带多尺度模糊熵融合的牵引整流器故障诊断 |
| Traction rectifier fault diagnosis based on multi-band and multi-scale fuzzy entropy fusion |
| 摘要点击 3129 全文点击 230 投稿时间：2024-04-28 修订日期：2025-05-03 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.40244 |
| 2025,42(7):1313-1322 |
| 中文关键词 牵引整流器 能熵比 多尺度模糊熵 能量 多信息融合 |
| 英文关键词 traction rectifier energy entropy ratio multi-scale fuzzy entropy energy multi-information fusion |
| 基金项目 甘肃省青年科技基金项目(24JRRA266)资助. |
|
| 中文摘要 |
| 针对电力机车牵引传动系统中故障率最高的牵引整流器, 本文提出了一种多频带多尺度模糊熵融合的故
障诊断方法. 首先, 在优选小波基函数的基础上, 小波包分解不同工况、不同运行模式下的故障信号, 得到最优多频
带信息; 其次, 对各频带的序列进行粗粒化处理, 计算多尺度模糊熵; 最后, 求解各频带多尺度模糊熵的能量值, 作
为故障特征向量. 结果表明, 基于最优小波基函数得到的多频带模糊熵特征对噪声具有一定的鲁棒性, 所提出的多
尺度模糊熵融合算法能进一步提高故障诊断率. 与其他方法相比, 该方法具有较高的诊断率和较强的鲁棒性. |
| 英文摘要 |
| Aiming at the traction rectifier with the highest failure rate in the traction transmission system of electric
locomotive, a fault diagnosis method based on multi-band and multi-scale fuzzy entropy fusion algorithm is proposed.
Firstly, based on the optimal wavelet basis function, wavelet packet decomposes fault signals under different working
conditions and different operating modes, and a series of optimal frequency bands information are obtained. Secondly, the
sequences of each frequency band are coarse-granulated and multi-scale fuzzy entropy is calculated. Finally, the energy
value of multi-scale fuzzy entropy of each frequency band is solved, which is used as the fault feature vector. The results
show that the multi-band fuzzy entropy feature based on the optimal wavelet basis function has a certain robustness to noise,
and according to the proposed multi-scale fuzzy entropy fusion algorithm, the fault diagnosis rate can be further improved.
Compared with other methods, the proposed method has higher diagnosis rate and stronger robustness. |
|
|
|
|
|