摘要
为解决编码器的瞬时角速度(Instantaneous Angular Speed,IAS)信号中旋转部件故障特征微弱的难题,本文提出一种平均降采样多周期微分均值(Average Down⁃Sampling Multi⁃period Differential Means,ADSMPDM)的故障特征增强方法。基于IAS信号的估计特性,开展了IAS信号的平均降采样研究,验证了平均降采样具有抑制随机噪声的特性;基于平均降采样抑制随机噪声特性、降低计算成本和减小存储空间的优势,结合多周期微分均值的累积特性,提出一种ADSMPDM算法对原始IAS信号中的旋转部件故障分量进行增强处理;通过阶次谱分析揭示故障特征。采用仿真数据和实验数据进行验证分析,并与快速谱峭度、可调整多点优化最小熵反卷积、离散随机分离和谱幅值调制算法进行对比,验证了ADSMPDM算法增强旋转部件故障特征的有效性和优势。
To address the issue of weak features related to faulty rotating parts in Instantaneous Angular Speed(IAS)signal,this study proposes a Average Down-Sampling Multi-Period Differential Means(ADSMPDM)scheme to enhance fault features.First⁃ly,based on the estimation characteristics of the IAS,the average down-sampling of the IAS signal is studied and its features of suppressing random noise are obtained.Secondly,the ADSMPDM scheme is proposed to enhance the features related to the fault in the IAS signal based on the advantages of the average down-sampling(such as noise suppression,low computational cost and low storage space)and accumulative characteristic of multi-period differential means.Finally,the features related to the fault are re⁃vealed by order spectrum analysis.By using Simulations and experiments and comparing with fast kurtogram,multipoint optimal minimum entropy deconvolution adjusted,discrete random separation and spectral amplitude modulation,the effectiveness and ad⁃vantages of the ADSMPDM algorithm in enhancing gear and bearing fault feature components are verified.
作者
陈鑫
郭瑜
CHEN Xin;GUO Yu(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,China)
出处
《振动工程学报》
EI
CSCD
北大核心
2024年第2期346-355,共10页
Journal of Vibration Engineering
基金
国家自然科学基金资助项目(52165067)
云南科技计划重大专项项目(202002AC080001)。
关键词
故障诊断
平均降采样多周期微分均值
编码器信号
瞬时角速度
特征提取
fault diagnosis
average down-sampling multi-period differential means
encoder signal
instantaneous angular speed
feature extraction
