基于变分模态分解和希尔伯特变换的转子非平稳信号故障特征识别OA北大核心CSTPCD
Fault feature identification for rotor nonstationary signals based on VMD-HT
为了提升传统希尔伯特黄变换在处理复杂非平稳信号时的时频分析能力,本文将变分模态分解和希尔伯特变换进行结合,提出了 一种时频分析方法变分模态分解和希尔伯特变换.此外,为了对变分模态分解的模态数进行自动调整,还提出了一种基于相关系数的希尔伯特黄变换参数优化方法,有效避免了由于希尔伯特黄变换模态数设置不合理而导致的信号分解不足和分解过剩的问题.利用转子故障信号对变分模态分解和希尔伯特变换方法的时频分析能力进行了验证,并且与传统希尔伯特黄变换的对比突出了该方法在处理非平稳信号中的优势.
The Hilbert-Huang transform(HHT)is a most representative time-frequency analysis method.To improve the time-frequency analysis performance of the traditional HHT in processing complex nonstationary sig-nals,herein,variational mode decomposition(VMD)and Hilbert transform(HT)are combined to form a new time-frequency analysis method of VMD-HT.Moreover,to automatically tune the parameter of the mode number in VMD,a parameter optimization method based on the correlation coefficient is proposed,which effectively solves the problems of insufficient decomposition and excessive decomposition of signals due to the unreasonable setting of the mode number of VMD.The time-frequency analysis performance of VMD-HT is validated using rotor fault signals,and comparisons with the traditional HHT highlight the advantages of this method in processing nonstationary signals.
朱少民;夏虹;尹文哲;王志超;张汲宇
上海交通大学船舶海洋与建筑工程学院,上海 200240||浙江清华柔性电子技术研究院,浙江嘉兴 314006哈尔滨工程大学核安全与先进核能技术工业和信息化部重点实验室,黑龙江哈尔滨 150001||哈尔滨工程大学核安全与仿真技术国防重点学科实验室,黑龙江哈尔滨 150001
机械工程
转子非平稳信号变分模态分解希尔伯特黄变换特征识别
rotornonstationary signalvariational mode decomposition(VMD)Hilbert-Huang transform(HHT)feature identification
《哈尔滨工程大学学报》 2024 (005)
825-832 / 8
国家自然科学基金项目(U21B2083);国防科技工业核动力技术创新中心项目(HDLCXZX-2021-ZH-019).
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