Adaptive feature extraction method for slewing bearing based on Wavelet leader and optimized isometric mapping method

doi:10.3785/j.issn.1008-973X.2019.11.006

Journal of ZheJiang University (Engineering Science)

2019, Vol. 53

Issue (11): 2092-2101 DOI: 10.3785/j.issn.1008-973X.2019.11.006

Mechanical Engineering

Adaptive feature extraction method for slewing bearing based on Wavelet leader and optimized isometric mapping method

Xiang-long ZHAO1(

),Jie CHEN1,2,*(

),Rong-jing HONG1,2,Hua WANG1,2,Yuan-yuan LI3

1. College of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China
2. Jiangsu Key Laboratory of Digital Manufacturing for Industrial Equipment and Control Technology, Nanjing 211816, China
3. Minth Group, Ningbo 315806, China

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Abstract

Multi-fractal adaptive feature extraction method based on Wavelet leader method and isometric mapping method optimized by hybrid grey wolf optimization algorithm (HGWO-ISOMAP) was proposed, in order to solve the problem that the vibration signal of slewing bearing is weak and the feature information is difficult to extract. Wavelet leader is utilized to calculate the multi-fractal features, mine the geometric structure information of vibration data, and construct a high-dimensional multi-fractal feature matrix. Adaptive feature selection of high-dimensional feature matrix is carried out through ISOMAP method optimized by HGWO. The selected feature matrix is input into the least squares support vector machine (LSSVM) optimized by genetic algorithm (GA) for fault state identification. A full life experiment of a certain type of slewing bearing was carried out by using self-developed comprehensive performance test platform of slewing bearing, in order to verify the superiority of the proposed method. Results show that compared with general time domain, time-frequency domain and frequency domain feature extraction methods, the proposed method can improve the recognition accuracy and reduce the calculation time, providing a new effective way for feature extraction of slewing bearing.

Key words： slewing bearing feature extraction multi-fractal feature Wavelet leader hybrid grey wolf optimization algorithm (HGWO) isometric mapping (ISOMAP)

Received: 11 September 2018 Published: 21 November 2019

CLC:	TH 133
	TN 911

Corresponding Authors: Jie CHEN E-mail: 2390698155@qq.com;nj_tech_cj@163.com

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	Xiang-long ZHAO
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Cite this article:

Xiang-long ZHAO,Jie CHEN,Rong-jing HONG,Hua WANG,Yuan-yuan LI. Adaptive feature extraction method for slewing bearing based on Wavelet leader and optimized isometric mapping method. Journal of ZheJiang University (Engineering Science), 2019, 53(11): 2092-2101.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.11.006 OR http://www.zjujournals.com/eng/Y2019/V53/I11/2092

基于Wavelet leader和优化的等距映射算法的回转支承自适应特征提取

为了解决回转支承振动信号微弱，特征信息不易提取的问题，提出基于Wavelet leader方法和经混合灰狼算法优化的等距映射算法（HGWO-ISOMAP）的多分形自适应特征提取方法. 利用Wavelet leader计算多分形特征，挖掘振动数据的几何结构信息，构造高维特征矩阵；通过HGWO优化后的ISOMAP算法对高维特征矩阵进行自适应特征筛选；将筛选后的特征矩阵输入到经遗传算法（GA）优化的最小二乘支持向量机（LSSVM）中进行故障状态识别. 为了验证所提方法的优越性，采用课题组自主研发的回转支承综合性能试验台对某型号回转支承进行全寿命实验. 结果表明，相比一般时域、时频域、频域特征提取方法，所提方法能提高识别精度，缩短计算时间，为回转支承特征提取提供新的有效途径.

关键词： 回转支承, 特征提取, 多分形特征, Wavelet leader, 混合灰狼优化算法(HGWO), 等距映射(ISOMAP)

Fig.1 Flow chart of adaptive feature extraction method of slewing bearing

Fig.2 Physical map of slewing bearing test rig

Fig.3 System structure flow chart of slewing bearing test rig

Tab.1 Structural parameters of a certain type of slewing bearing

Fig.4 Acceleration signal map of slewing bearing

Fig.5 Comparison of vibration signal before and after noise reduction

Fig.6 Multifractal feature map in three states

Fig.7 Multifractal spectrum-singular exponential graph of a single sample

Fig.8 Statistical results of special point distribution in three states

Tab.2 Statistical results of special point distribution intervals

Fig.9 Dimension reduction results of HGWO-ISOMAP method

Fig.10 Data visualization after dimensionality reduction

Fig.11 Optional three-dimensional data visualization after dimensionality reduction

Fig.12 Classification results of LS-SVM method

Tab.3 Recognition results of proposed method

Tab.4 Recognition results of other methods


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