Aero-engine Fault Diagnosis Based on an Enhanced Minimum Entropy Deconvolution

被引：0
|
作者：
Zhao Y. [1 ]
Wang J. [1 ,2 ]
Zhang X. [1 ,2 ]
Wu L. [1 ]
Liu Z. [3 ]
机构：
[1] School of Mechanical Engineering, Southwest Jiaotong University, Chengdu
[2] State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing
[3] School of Automation Engineering, University of Electronic and Technology of China, Chengdu
来源：
Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2023年 / 34卷 / 02期
关键词：
aero-engine; bearing; fault diagnosis; minimum entropy deconvolution; unbiased autocorrelation;
D O I：
10.3969/j.issn.1004-132X.2023.02.009
中图分类号：
学科分类号：
摘要：
To extract weak features of bearing faults， an enhanced minimum entropy deconvolution method was proposed based on unbiased autocorrelation analysis. In iterative solution of filter coefficients for the method， aperiodic components in the filtered signals were suppressed， the enhanced detection of the periodic fault impact features was realized， and the accurate identification of bearing faults was completed. The analysis results of simulated signals show that the enhanced minimum entropy deconvolution may accurately extract the periodic fault impact sequence from the signals with complex interferences. The applications in aero-engine fault diagnosis verify the effectiveness of the method for fault diagnosis of bearings in complex mechanical structures. © 2023 China Mechanical Engineering Magazine Office. All rights reserved.
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页码：193 / 200
页数：7
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