Fault diagnosis for gearboxes based on Fourier decomposition method and resonance demodulation

IF 3.3 3区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY Journal of Zhejiang University-SCIENCE A Pub Date : 2023-04-19 DOI:10.1631/jzus.A2200555
Shuiguang Tong, Zilong Fu, Zhe-ming Tong, Junjie Li, F. Cong
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引用次数: 1

Abstract

Condition monitoring and fault diagnosis of gearboxes play an important role in the maintenance of mechanical systems. The vibration signal of gearboxes is characterized by complex spectral structure and strong time variability, which brings challenges to fault feature extraction. To address this issue, a new demodulation technique, based on the Fourier decomposition method and resonance demodulation, is proposed to extract fault-related information. First, the Fourier decomposition method decomposes the vibration signal into Fourier intrinsic band functions (FIBFs) adaptively in the frequency domain. Then, the original signal is segmented into short-time vectors to construct double-row matrices and the maximum singular value ratio method is employed to estimate the resonance frequency. Then, the resonance frequency is used as a criterion to guide the selection of the most relevant FIBF for demodulation analysis. Finally, for the optimal FIBF, envelope demodulation is conducted to identify the fault characteristic frequency. The main contributions are that the proposed method describes how to obtain the resonance frequency effectively and how to select the optimal FIBF after decomposition in order to extract the fault characteristic frequency. Both numerical and experimental studies are conducted to investigate the performance of the proposed method. It is demonstrated that the proposed method can effectively demodulate the fault information from the original signal. 目 的 齿轮箱的振动信号频谱结构比较复杂, 难以提取其故障特征频率. 傅里叶分解方法可以将振动信号分解为多个单分量信号, 利用共振频率筛选出最优分量并进行包络解调, 识别特征频率以实现故障诊断. 创新点 1. 为了求解共振频率, 提出一种基于短时向量的最大奇异值比方法; 2. 将傅里叶分解方法引入到齿轮箱故障诊断中, 并利用共振频率选择最优分量进行包络解调以提取故障特征频率. 方 法 1. 分析奇异值比与冲击信号的关系, 提出求解共振频率的最大奇异值比方法; 2. 对比最大奇异值比方法与谱峭度方法在求解共振频率方面的表现, 从而验证最大奇异值比方法的有效性; 3. 对比分析所提方法与传统的总体经验模态分解 (EEMD) 和变分模态分解 (VMD) 方法在信号分解与故障特征提取方面的效果, 并通过仿真和实验进行验证. 结 论 1. 最大奇异值比方法能够准确计算出共振频率, 比谱峭度方法求解的频率值更加精确; 2. 基于傅里叶分解方法和最大奇异值比的共振解调方法能够有效提取故障特征频率, 其在故障诊断方面的表现优于EEMD和VMD方法.
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基于傅里叶分解和共振解调的齿轮箱故障诊断
齿轮箱的状态监测和故障诊断在机械系统维护中起着重要的作用。齿轮箱振动信号具有复杂的频谱结构和较强的时变特性,这给故障特征提取带来了挑战。针对这一问题,提出了一种基于傅里叶分解和共振解调的故障相关信息提取方法。首先,傅里叶分解方法在频域自适应地将振动信号分解为傅里叶内禀带函数(fifif);然后,将原始信号分割成短时间向量构造双行矩阵,并采用最大奇异值比法估计共振频率;然后,以共振频率为标准,指导选择最相关的FIBF进行解调分析。最后,对最优FIBF进行包络解调,识别故障特征频率。主要贡献在于该方法描述了如何有效地获得共振频率以及如何在分解后选择最优的FIBF以提取故障特征频率。通过数值和实验研究验证了该方法的性能。实验表明,该方法能有效地解调原始信号中的故障信息。目 的 齿轮箱的振动信号频谱结构比较复杂, 难以提取其故障特征频率. 傅里叶分解方法可以将振动信号分解为多个单分量信号, 利用共振频率筛选出最优分量并进行包络解调, 识别特征频率以实现故障诊断. 创新点 1. 为了求解共振频率, 提出一种基于短时向量的最大奇异值比方法; 2. 将傅里叶分解方法引入到齿轮箱故障诊断中, 并利用共振频率选择最优分量进行包络解调以提取故障特征频率. 方 法 1. 分析奇异值比与冲击信号的关系, 提出求解共振频率的最大奇异值比方法; 2. 对比最大奇异值比方法与谱峭度方法在求解共振频率方面的表现, 从而验证最大奇异值比方法的有效性; 3.对比分析所提方法与传统的总体经验模态分解(EEMD)和变分模态分解(VMD)方法在信号分解与故障特征提取方面的效果,并通过仿真和实验进行验证。结 论 1. 最大奇异值比方法能够准确计算出共振频率, 比谱峭度方法求解的频率值更加精确; 2. 基于傅里叶分解方法和最大奇异值比的共振解调方法能够有效提取故障特征频率,其在故障诊断方面的表现优于EEMD和VMD方法。
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来源期刊
Journal of Zhejiang University-SCIENCE A
Journal of Zhejiang University-SCIENCE A 工程技术-工程:综合
CiteScore
5.60
自引率
12.50%
发文量
2964
审稿时长
2.9 months
期刊介绍: Journal of Zhejiang University SCIENCE A covers research in Applied Physics, Mechanical and Civil Engineering, Environmental Science and Energy, Materials Science and Chemical Engineering, etc.
期刊最新文献
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