反褶积
脉冲(物理)
自相关
Morlet小波
断层(地质)
振幅
小波
盲反褶积
能量(信号处理)
控制理论(社会学)
数学
算法
计算机科学
工程类
统计
物理
小波变换
人工智能
离散小波变换
地质学
控制(管理)
量子力学
地震学
作者
Jinxi Wang,Faye Zhang,Lei Zhang,Mingshun Jiang
标识
DOI:10.1016/j.aei.2022.101721
摘要
Periodic fault impulses, inevitably occurring along with a localized defect, are crucial for incipient fault diagnosis of rotating machinery. Whereas, they are awfully weak and masked by other interferences in industrial applications. Blind deconvolution methods (BDMs) are diffusely used in enhancing periodic fault impulses submerged in vibration signals. Due to some drawbacks, the applications of traditional BDMs are restricted. Therefore, the maximum average impulse energy ratio deconvolution (MAIERD) method is proposed, where the maximization of a new index called average impulse energy ratio (AIER) is tailored as the objective function. AIER takes the sampling point with the largest amplitude in every fault period as the location of fault impulse. Also, the fault period is detected by the autocorrelation function of the envelope signal. Furthermore, the Morlet wavelet is appointed as the initial filter. The synthesized signals and experimental data collected from two different rolling bearing test rigs are processed for verification. The results show that the proposed MAIERD method is superior in enhancing periodic fault impulses compared with five popular deconvolution methods.
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