Amplitude-cyclic frequency decomposition of vibration signals for bearing fault diagnosis based on phase editing

L. Barbini; M. Eltabach; A.J. Hillis; J.L du Bois

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Amplitude-cyclic frequency decomposition of vibration signals for bearing fault diagnosis based on phase editing

机译：基于相位编辑的轴承故障诊断振动信号幅频分解

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In rotating machine diagnosis different spectral tools are used to analyse vibration signals. Despite the good diagnostic performance such tools are usually refined, computationally complex to implement and require oversight of an expert user. This paper introduces an intuitive and easy to implement method for vibration analysis: amplitude cyclic frequency decomposition. This method firstly separates vibration signals accordingly to their spectral amplitudes and secondly uses the squared envelope spectrum to reveal the presence of cyclostationarity in each amplitude level. The intuitive idea is that in a rotating machine different components contribute vibrations at different amplitudes, for instance defective bearings contribute a very weak signal in contrast to gears. This paper also introduces a new quantity, the decomposition squared envelope spectrum, which enables separation between the components of a rotating machine. The amplitude cyclic frequency decomposition and the decomposition squared envelope spectrum are tested on real word signals, both at stationary and varying speeds, using data from a wind turbine gearbox and an aircraft engine. In addition a benchmark comparison to the spectral correlation method is presented.

机译：在旋转机器诊断中，使用不同的频谱工具来分析振动信号。尽管具有良好的诊断性能，但此类工具通常仍需要完善，计算复杂才能实现，并且需要专家用户的监督。本文介绍了一种直观且易于实现的振动分析方法：幅度循环频率分解。该方法首先根据振动信号的频谱振幅分离振动信号，其次使用平方包络频谱揭示每个振幅水平上的循环平稳性。直观的想法是，在旋转机械中，不同的组件会以不同的振幅产生振动，例如，与齿轮相比，有缺陷的轴承会产生非常弱的信号。本文还介绍了一个新的数量，即分解平方包络谱，它可以使旋转机器的各个组件分离。使用来自风力涡轮机变速箱和飞机发动机的数据，以固定速度和可变速度在实字信号上测试幅度循环频率分解和分解平方包络谱。另外，提出了与频谱相关方法的基准比较。

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来源
《Mechanical systems and signal processing》 |2018年第15期|76-88|共13页
作者
L. Barbini; M. Eltabach; A.J. Hillis; J.L du Bois;
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作者单位

The University of Bath Department of Mechanical Engineering, Claverton Down, Bath BA2 7AY, UK;

Centre Technique des Industries Mecaniques, Felix-Louat, Senlis 60300, France;

The University of Bath Department of Mechanical Engineering, Claverton Down, Bath BA2 7AY, UK;

The University of Bath Department of Mechanical Engineering, Claverton Down, Bath BA2 7AY, UK;

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正文语种 eng
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关键词
Phase editing; Diagnostics of defective bearings; Spectra correlation; Enhanced squared envelope spectrum; Variable speed;

机译：阶段编辑;诊断轴承故障;光谱]相关性;增强的平方包络谱;变速;

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专利

1. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals [J] . Ben Ali Jaouher, Fnaiech Nader, Saidi Lotfi, Applied Acoustics . 2015,第mara期

机译：经验模态分解和人工神经网络在振动信号自动诊断中的应用
2. Semi-automated diagnosis of bearing faults based on a hidden Markov model of the vibration signals [J] . Xin Ge, Hamzaoui Nacer, Antoni Jerome Measurement . 2018,第期

机译：基于隐马尔可夫振动信号模型的半自动诊断轴承故障
3. Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis [J] . Lotfi Saidi, Jaouher Ben Ali, Farhat Fnaiech ISA Transactions . 2014,第5期

机译：基于双谱的EMD应用于非平稳振动信号的轴承故障诊断
4. ALGORITHM FOR MULTIPLE TIME-FREQUENCY CURVE EXTRACTION FROM TIME-FREQUENCY REPRESENTATION OF VIBRATION SIGNALS FOR BEARING FAULT DIAGNOSIS UNDER TIME-VARYING SPEED CONDITIONS [C] . Huan Huang, Natalie Baddour, Ming Liang Proceedings of the ASME international design engineering technical conferences and computers and information in engineering conference 2017 . 2017

机译：时变速度条件下从振动信号的时频表示中提取多个时频曲线以进行故障诊断的算法
5. Fault detection, diagnosis and prognosis of rolling element bearings: Frequency domain methods and hidden Markov modeling. [D] . Ocak, Hasan. 2004

机译：滚动轴承的故障检测，诊断和预测：频域方法和隐马尔可夫建模。
6. Bearing Fault Diagnosis Based on the Switchable Normalization SSGAN with 1-D Representation of Vibration Signals as Input [O] . Dongdong Zhao, Feng Liu, He Meng 2019

机译：基于振动信号一维表示作为输入的可切换归一化SSGAN的轴承故障诊断
7. A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals [O] . Yi Gu, Jiawei Cao, Xin Song, 2021

机译：用于时域振动信号的基于去噪的基于AutoEncoder的轴承故障诊断系统

Amplitude-cyclic frequency decomposition of vibration signals for bearing fault diagnosis based on phase editing

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