A novel compound fault diagnosis for rolling bearing based on sparse harmonic feature mode decomposition and enhanced spectral amplitude modulation

IF 8.9 1区 工程技术 Q1 ENGINEERING, MECHANICAL
Weiliang Sun, Zong Meng, Jingbo Liu, Dengyun Sun, Kai Chen, Yonglei Ren
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引用次数: 0

Abstract

To effectively tackle the challenge of accurately extracting compound fault features from rolling bearing vibration signals, a novel method termed sparse harmonic feature mode decomposition and enhanced spectral amplitude modulation is proposed. The method combines the superior mode extraction capability of feature mode decomposition with the nonlinear feature identification advantage of spectral amplitude modulation. First, a sparsity factor is introduced to refine the output signal of a finite impulse response filter, enhancing the sparsity of the signal. Second, the filter coefficients are optimized with the envelope harmonic noise ratio as the objective function, which facilitates the extraction of essential feature components. Then, the extracted components undergo nonlinear frequency and amplitude modulation. The spectral coherence of the modulate modal signals is calculated to capture modulation characteristics. Finally, an enhanced envelope spectrum strategy is adopted to improve the accuracy of fault feature identification. Results from both simulations and experimentals confirm that the SHFMD-ESAM method can accurately separate and identify multiple compound fault features in rolling bearings. Compared with existing methods, it achieves higher accuracy and robustness, demonstrating strong potential for rolling bearing fault diagnosis.
基于稀疏谐波特征模态分解和增强谱调幅的滚动轴承复合故障诊断方法
为了有效地解决从滚动轴承振动信号中准确提取复合故障特征的难题,提出了一种稀疏谐波特征模态分解和增强谱调幅的新方法。该方法结合了特征模态分解优越的模态提取能力和频谱调幅的非线性特征识别优势。首先,引入稀疏度因子对有限脉冲响应滤波器的输出信号进行细化,增强了信号的稀疏度。其次,以包络谐波噪声比为目标函数对滤波器系数进行优化,便于提取基本特征分量;然后,对提取的分量进行非线性调频和调幅。计算调制模态信号的频谱相干性以捕获调制特性。最后,采用增强包络谱策略提高故障特征识别的准确率。仿真和实验结果表明,SHFMD-ESAM方法能够准确分离和识别滚动轴承的多个复合故障特征。与现有方法相比,该方法具有更高的精度和鲁棒性,在滚动轴承故障诊断中具有较强的应用潜力。
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来源期刊
Mechanical Systems and Signal Processing
Mechanical Systems and Signal Processing 工程技术-工程:机械
CiteScore
14.80
自引率
13.10%
发文量
1183
审稿时长
5.4 months
期刊介绍: Journal Name: Mechanical Systems and Signal Processing (MSSP) Interdisciplinary Focus: Mechanical, Aerospace, and Civil Engineering Purpose:Reporting scientific advancements of the highest quality Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems
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