Gearbox Fault Diagnosis Based on ALIF-PE-GOLSSVM

Huang Ying; Li Ximei; Ye Renhu; Wang Rui

doi:10.16578/j.issn.1004.2539.2022.11.023

您当前的位置：

首页 >

文章列表页 >

Gearbox Fault Diagnosis Based on ALIF-PE-GOLSSVM

Development·Application | 更新时间：2022-12-01

- Gearbox Fault Diagnosis Based on ALIF-PE-GOLSSVM
- Journal of Mechanical Transmission Vol. 46, Issue 11, Pages: 146-153(2022)
- 作者机构：
  
  1.武汉华夏理工学院智能制造学院，湖北武汉 430223
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2022.11.023
  CLC：
扫描看全文
黄英,李喜梅,叶仁虎等.基于ALIF-PE-GOLSSVM的齿轮箱故障诊断[J].机械传动,2022,46(11):146-153.

Huang Ying,Li Ximei,Ye Renhu,et al.Gearbox Fault Diagnosis Based on ALIF-PE-GOLSSVM[J].Journal of Mechanical Transmission,2022,46(11):146-153.
黄英,李喜梅,叶仁虎等.基于ALIF-PE-GOLSSVM的齿轮箱故障诊断[J].机械传动,2022,46(11):146-153. DOI： 10.16578/j.issn.1004.2539.2022.11.023.

Huang Ying,Li Ximei,Ye Renhu,et al.Gearbox Fault Diagnosis Based on ALIF-PE-GOLSSVM[J].Journal of Mechanical Transmission,2022,46(11):146-153. DOI： 10.16578/j.issn.1004.2539.2022.11.023.

摘要

提出了基于基因优化最小二乘支持向量机（Gene optimized least squares support vector machine，GOLSSVM）的自适应局部迭代滤波（Adaptive local iterative fittering，ALIF）和排列熵（Permutation entropy，PE）的故障诊断方法，并将该方法应用于齿轮箱的诊断，成功实现了对齿轮箱4种故障种类的识别。针对排列熵无法直接识别齿轮箱不同故障类别的问题，利用ALIF方法相较于EMD方法在去除残余噪声及抑制模式混叠上的优势，使用ALIF方法对故障信号进行降噪，提取有效分量，再计算有分量的PE值（C-PE值），以获得振动信号的多尺度特性；然后，使用基因算法对最小二乘支持向量机（Least squares support vector machine，LSSVM）进行了优化；最后，将特征向量输入到GOLSSVM，对故障特征进行分类。结果表明，所提方法相比BP神经网络和SVM在故障识别精度上有优势。

Abstract

An adaptive local iterative filtering (ALIF) and permutation entropy (PE) fault diagnosis method based on gene optimized least squares support vector machine (GOLSSVM) is proposed. The method is applied to the diagnosis of gearboxes, and the identification of four fault types of gearboxes is successfully realized. Aiming at the problem that permutation entropy cannot directly identify different fault categories of gearboxes, the advantages of ALIF method in removing residual noise and suppressing mode aliasing compared with EMD method are used, and the ALIF method is used to reduce noise and extract effective components. Then the PE value with component (C-PE value) is calculated to obtain the multi-scale characteristics of vibration signals. Then the genetic algorithm is used to optimize the least squares support vector machine (LSSVM). Finally, the feature vector is input into GOLSSVM to classify the fault features. The results show that the proposed method has advantages in fault recognition accuracy compared with BP neural network and SVM.

关键词

基因优化支持向量机自适应局部迭代滤波排列熵

Keywords

Gene optimizationSupport vector machineAdaptive local iterative filteringPermutation entropy

references

LEE D H，AHN J H，KOH B H.Fault detection of bearing systems through EEMD and optimization algorithm［J］.Sensors，2017，17（11）：2477.

ZHU M，LIU N.Research on NMR noise reduction method based on improved CEEMD［J］.IEEE Access，2020（8）：122864-122873.

GUO W X，LI R Q，KOU Y F，et al.Application research of a new adaptive noise reduction method in fault diagnosis［J］.Applied Sciences，2020，10（15）：5078.

LIN L，WANG Y，ZHOU H M.Iterative filtering as an alternative algorithm for empirical mode decomposition［J］.Advances in Adaptive Data Analysis，2009，1（4）：543-560.

CICONE A，LIU J F，ZHOU H M.Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis［J］.Applied and Computational Harmonic Analysis，2016，41（2）：384-411.

PIERSANTI M，CICONE A，MATERSSI M，et al.Adaptive local iterative filtering：a promising technique for the analysis of non-stationary signals［C］//European Geoscience Union，2018：1031-1046.

唐贵基，庞彬.基于ALIF-HT的汽轮发电机组转子故障诊断［J］.动力工程学报，2017，37（11）：883-889.

TANG Guiji，PANG Bin.Rotor fault diagnosis of steam turbine generator unit based on ALIF-HT［J］.Journal of Power Engineering，2017，37（11）：883-889.

BANDT C，POMPE B.Permutation entropy：a natural complexity measure for time series［J］.Physical Review Letters，2002，88（17）：1-4.

XIAO L，LV Y，FU G.Fault classification of rotary machinery based on smooth local subspace projection method and permutation entropy［J］.Applied Sciences，2019，9（10）：2102.

LI B，ZHANG P，REN G，et al.Gear fault diagnosis using empirical mode decomposition，genetic algorithm and support vector machine［J］.Journal of Vibration，Measurement and Diagnosis，2009（4）：445-448.

CAO Y，TUNG W，GAO J B，et al.Detecting dynamical changes in time series using the permutation entropy［J］.Physical Review E，2004，70（4 Pt 2）：046217.

YAN R，GAO R X.Approximate entropy as a diagnostic tool for machine health monitoring［J］.Mechanical Systems and Signal Processing，2007，21（2）：824-839.

ZHANG L，XIONG G，LIU H，et al.Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference［J］.Expert Systems with Applications，2010，37（8）：6077-6085.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM

Fault Diagnosis of Gearboxes Based on AO-VMD and IAO-SVM

Gearbox Fault Diagnosis Based on Dynamic Weighted Feature Fusion with Maximum Information Coefficient

Fault Diagnosis Method of Planetary Gearboxes Based on LMD Permutation Entropy and BP Neural Network

Gear Pitting Fault Diagnosis Based on Improved Variational Modal Decomposition

Related Author

No data

Related Institution

School of Electrical Engineering, Xinjiang University

Zhengzhou Research Institute of Mechanical Engineering Co., Ltd.

School of Mechanical and Electrical Engineering, Henan University of Technology

Department of Mechanical Engineering, Guangzhou Maritime University

Guoneng Yunnan New Energy Co., Ltd.

⁰