Importance of the fourth and fifth intrinsic mode functions for bearing fault diagnosis

机译：第四和第五固有模式函数在轴承故障诊断中的重要性

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In the most of industrial and domestic applications bearings present important assets. The diagnostic of these elements needs accurate and reliable acquisition of its dynamic vibration signals affected by noise and other part of system such as gears, bars… Empirical Mode Decomposition (EMD) is a new signal processing method used to decompose non-stationary and non-linear vibration bearing signals into several stationary empirical mode components called Intrinsic Mode Functions (IMF). For each IMF, the energy entropy mean is computed. This technique is compared to the most used statistical features (RMS, Kurtosis) using a characterization degree. Experimental results show that time domain feature extraction is effective for bearing fault feature extraction as type (inner race, outer race, rolling element) and severity (normal, degraded, faulting). The choice of the most significant IMFs is also discussed in this paper.

机译：在大多数工业和家庭应用中，轴承都是重要资产。对这些元素的诊断需要准确，可靠地获取受噪声和系统其他部分（例如齿轮，杆）影响的动态振动信号。经验模态分解（EMD）是一种用于分解非平稳和非平稳信号的新信号处理方法。线性振动轴承将信号转换成几个称为固有模式函数（IMF）的固定经验模式分量。对于每个IMF，计算能量熵均值。使用表征度将该技术与最常用的统计特征（RMS，峰度）进行比较。实验结果表明，时域特征提取对于类型（内圈，外圈，滚动体）和严重性（正常，退化，断层）的轴承故障特征提取是有效的。本文还将讨论最重要的IMF的选择。

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来源
《2013 14th International Conference on Sciences and Techniques of Automatic Control amp; Computer Engineering》|2013年|259-264|共6页
会议地点 Sousse(TN)
作者
Benali Jaouher; Sayadi Mounir; Fnaiech Farhat; Morello Brigitte;
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作者单位

University of Tunis, Higher School of Sciences and Techniques of Tunis, Laboratory of Signal Image and Energy Mastery (SIME), 5 Av. Taha Hussein, 1008 Tunis, Tunisiac;

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原文格式 PDF
正文语种 eng
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关键词
Rolling element bearing; degree of characterization; empirical mode decomposition; energy entropy; fault feature extraction;

机译：滚动轴承；特征度；经验模态分解；能量熵；故障特征提取；;

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专利

1. Selecting effective intrinsic mode functions of empirical mode decomposition and variational mode decomposition using dynamic time warping algorithm for rolling element bearing fault diagnosis [J] . Kumar Prem Shankar, Kumaraswamidhas Lakshmi Annamalai, Laha Swarup Kumar Transactions of the Institute of Measurement and Control . 2019,第7期

机译：使用动态时间翘曲算法选择经验模式分解的有效内在模式函数和变分模式分解滚动元件故障诊断
2. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform [J] . V.K. Rai, A.R. Mohanty Mechanical systems and signal processing . 2007,第6期

机译：Hilbert-Huang变换中使用固有模式函数的FFT进行轴承故障诊断
3. Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder [J] . Juying Dai, Jian Tang, Faming Shao, Applied Sciences . 2019,第13期

机译：基于多尺度内在模式功能置换熵的滚动轴承故障诊断和堆积稀疏的自动化器
4. Importance of the fourth and fifth intrinsic mode functions for bearing fault diagnosis [C] . Benali Jaouher, Sayadi Mounir, Fnaiech Farhat, International Conference on Sciences and Techniques of Automatic Control Computer Engineering . 2013

机译：第四和第五个内在模式的重要性轴承故障诊断
5. Analysis of rolling element bearing faults in rotating machinery: Experiments, modeling, fault detection and diagnosis. [D] . Adams, Michael Louis. 2001

机译：旋转机械中滚动轴承故障的分析：实验，建模，故障检测和诊断。
6. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis [O] . Bach Phi Duong, Jong-Myon Kim 2018

机译：轴承故障诊断组合模式的非互斥深层神经网络分类器
7. Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder [O] . 2019

机译：基于多尺度内在模式功能置换熵的滚动轴承故障诊断和堆积稀疏的自动化器
8. Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine [R] . Duyar, A., Guo, T.-H., Merrill, W., 1992

机译：基于模型的航天飞机主机驱动故障故障检测与诊断的实现

Importance of the fourth and fifth intrinsic mode functions for bearing fault diagnosis

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