首页|重加权Infogram算法及其在轴承故障诊断中的应用

重加权Infogram算法及其在轴承故障诊断中的应用

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针对轴承故障诊断中传统脉冲量化指标性能受限,无法正确指示在强背景噪声掩盖下的轴承故障频带的难题,提出了重加权平方包络负熵(reweighted negentropy of the squared envelope,RNSE)和重加权平方包络谱负熵(reweighted negentropy of the squared envelope spectrum,RNSES),它们不仅能够在无周期先验知识情况下保持对故障周期性脉冲敏感性,而且对于随机脉冲也有较强的鲁棒性.进一步地,为提取轴承振动信号中的故障特征,基于RNSE和RNSES的加权平均值提出了重加权信息图(reweighted infogram,Rinfogram)算法.利用轴承故障仿真信号和高速列车牵引电机轴承台架试验信号证明Rinfogram算法能够在强噪声干扰下成功识别故障频带,对于随机脉冲干扰具有很好的鲁棒性,其故障特征提取效果优于基于谱峭度的Kurtogram和传统Infogram,从而提高了轴承故障诊断的准确性.
Algorithm of Reweighted-infogram and Its Application in Bearing Fault Diagnosis
Traditional impulse quantization index in bearing fault diagnosis is limited in performance which can't correctly indicate the bearing fault bands under the mask of strong background noise.To solve this problem,RNSE(reweighted negentropy of the squared enve-lope)and RNSES(reweighted negentropy of the square envelope spectrum)were proposed which were sensitive to faulty periodic impulses and robust to random impulses without prior knowledge of the fault periodicity.Furthermore,the Rinfogram(reweighted Infogram)was pro-posed based on RNSE and RNSES to extract the fault features from bearing vibration signals.Validations on bearing fault simulation signals and high-speed train traction motor bearing bench test signals showed that Rinfogram was robust to random pulses and could successfully identify the fault frequency bands under strong noise interference.There is evidence that Rinfogram is better than Kurtogram based on spec-tral kurtosis and traditional Infogram in fault feature extraction effect,which improves the accuracy of bearing fault diagnosis.

RNSE(reweighted negentropy of the squared envelope)RNSES(reweighted negentropy of the squared envelope spec-trum)reweighted Infogrambearing fault diagnosis

徐五一、杨岗、邓琴、成雷

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西南交通大学唐山研究院,唐山 063000

西南交通大学机械工程学院,成都 610036

重加权平方包络负熵(RNSE) 重加权平方包络谱负熵(RNSES) 重加权Infogram 轴承故障诊断

国家重点研发计划四川省重点研发项目

2020YFB1200300ZL2023YFG0063

2024

10.12404/j.issn.1671-1815.2305583

科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子：0.338
ISSN：1671-1815
年,卷(期)：2024.24(22)