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

doi:10.3969/j.issn.1004-132X.2023.02.009

Abstract

Abstract: 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.

Key words: aero-engine, fault diagnosis, bearing, minimum entropy deconvolution, unbiased autocorrelation

摘要： 为有效提取轴承的微弱故障特征，提出一种基于无偏自相关分析的增强最小熵解卷积方法。该方法的滤波器系数的迭代求解中，通过抑制滤波信号中的非周期成分，实现对周期性故障冲击的增强检测，完成轴承故障的准确辨识。仿真信号分析结果表明，所提方法在复杂干扰下仍能准确提取轴承故障冲击序列。航空发动机故障诊断案例分析证实了该方法对复杂机械结构中轴承故障诊断的有效性。

关键词: 航空发动机, 故障诊断, 轴承, 最小熵解卷积, 无偏自相关

CLC Number:

V233.1

ZHAO Yike, WANG Jiaxu, ZHANG Xin, WU Lei, LIU Zhiwen. Aero-engine Fault Diagnosis Based on an Enhanced Minimum Entropy Deconvolution[J]. China Mechanical Engineering, 2023, 34(02): 193-200.

赵艺珂, 王家序, 张新, 吴磊, 刘治汶. 基于增强最小熵解卷积的航空发动机故障诊断[J]. 中国机械工程, 2023, 34(02): 193-200.

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