反褶积
盲反褶积
轴
方位(导航)
断层(地质)
包络线(雷达)
平滑度
工程类
控制理论(社会学)
计算机科学
算法
数学
结构工程
人工智能
地质学
数学分析
电信
雷达
控制(管理)
地震学
作者
Bingyan Chen,Fengshou Gu,Weihua Zhang,Mengying Tan,Yi Luo,Zuolu Wang,Zewen Zhou
出处
期刊:Mechanisms and machine science
日期:2023-01-01
卷期号:: 447-457
标识
DOI:10.1007/978-3-031-26193-0_38
摘要
Blind deconvolution is a widely used technique for fault diagnosis of rolling bearings. Traditional blind deconvolution methods, such as minimum entropy deconvolution, are susceptible to random transients, making it difficult to extract fault features of railway train axle bearings under strong external shock conditions. Deconvolution methods that take the fault characteristic frequency of interest as an input parameter, such as maximum second-order cyclostationarity blind deconvolution, can alleviate this deficiency, however, the bearing fault features are difficult to be extracted when the specified characteristic frequency deviates from the actual value greatly. To overcome these problems, the modified smoothness index of the squared envelope and the modified smoothness index of the squared envelope spectrum are proposed as objective functions of the deconvolution algorithms, allowing two new blind deconvolution methods to be developed for railway axle bearing faults diagnosis. The two proposed blind deconvolution methods are robust to random transients and do not require the characteristic frequency of interest as an input parameter. The fault diagnosis performance of the two proposed methods is verified using the experimental data of actual railway axle bearings and compared with the state-of-the-art deconvolution methods. The results show that the two proposed blind deconvolution methods can adaptively extract repetitive transient features from noisy vibration signals and effectively diagnose different faults of railway axle bearings.
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