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 |
|
|
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.
|
Received: 11 September 2018
Published: 21 November 2019
|
|
Corresponding Authors:
Jie CHEN
E-mail: 2390698155@qq.com;nj_tech_cj@163.com
|
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)
|
|
[1] |
张慧芳, 陈捷 大型回转支承故障信号处理方法综述[J]. 机械设计与制造, 2012, (3): 216- 218 ZHANG Hui-fang, CHEN Jie Research on signal processing method of large slewing bearing[J]. Machinery Design and Manufacture, 2012, (3): 216- 218
doi: 10.3969/j.issn.1001-3997.2012.03.080
|
|
|
[2] |
刘志军.风电回转支承监测与故障诊断研究[D]. 南京: 南京工业大学, 2011. LIU Zhi-jun. Research of monitoring and fault diagnosis of slewing bearing in wind turbine [D]. Nanjing: Nanjing Tech University, 2011.
|
|
|
[3] |
JAOUHER B, NADER F, LOTFI S, et al Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals[J]. Applied Acoustics, 2015, 89 (3): 16- 27
|
|
|
[4] |
YANG Y, YU D, CHENG J A roller bearing fault diagnosis method based on EMD energy entropy and ANN[J]. Journal of Sound and Vibration, 2006, 294 (1/2): 269- 277
|
|
|
[5] |
魏永合, 王明华 基于EEMD和SVM的滚动轴承退化状态识别[J]. 计算机集成制造系统, 2015, 21 (9): 2475- 2483 WEI Yong-he, WANG Ming-hua Degradation state recognition of rolling bearing based on EEMD and SVM[J]. Computer Integrated Manufacturing Systems, 2015, 21 (9): 2475- 2483
|
|
|
[6] |
陆超, 陈捷, 洪荣晶 采用概率主成分分析的回转支承寿命状态识别[J]. 西安交通大学学报, 2015, 49 (10): 90- 96 LU Chao, CHEN Jie, HONG Rong-jing Recognition of life state for slewing bearings using probabilistic component analysis[J]. Journal of Xi’an Jiaotong University, 2015, 49 (10): 90- 96
doi: 10.7652/xjtuxb201510015
|
|
|
[7] |
LU C, CHEN J, HONG R, et al Degradation trend estimation of slewing bearing based on LSSVM model[J]. Mechanical Systems and Signal Processing, 2016, 76/77: 353- 366
doi: 10.1016/j.ymssp.2016.02.031
|
|
|
[8] |
张淑清, 李盼, 胡永涛, 等 多重分形近似熵与减法FCM聚类的研究及应用[J]. 振动与冲击, 2015, 34 (18): 205- 209 ZHANG Shu-qing, LI Pan, HU Yong-tao, et al Application of multifractal approximate entropy and subtractive FCM clustering in gearbox fault diagnosis[J]. Journal of Vibration and Shock, 2015, 34 (18): 205- 209
|
|
|
[9] |
林近山, 陈前 基于多重分形去趋势波动分析的齿轮箱故障特征提取方法[J]. 振动与冲击, 2013, 32 (2): 97- 101 LIN Jin-shan, CHEN Qian Fault feature extraction of gearboxes based on multifractal detrended fluctuation analysis[J]. Journal of Vibration and Shock, 2013, 32 (2): 97- 101
doi: 10.3969/j.issn.1000-3835.2013.02.019
|
|
|
[10] |
LIN J, CHEN Q Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion[J]. Mechanical Systems and Signal Processing, 2013, 38 (2): 515- 533
doi: 10.1016/j.ymssp.2012.12.014
|
|
|
[11] |
XIONG Q, ZHANG W, LU T, et al A fault diagnosis method for rolling bearings based on feature fusion of multifractal detrended fluctuation analysis and alpha stable distribution[J]. Shock and Vibration, 2016, (3): 1- 12
|
|
|
[12] |
WENDT H, ABRY P. Bootstrap for multifractal analysis [C] // 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings. Toulous: Institute of Electrical and Electronics Engineers, 2006: 38-48.
|
|
|
[13] |
WENDT H, ABRY P. Bootstrap tests for the time constancy of multifractal attributes [C]// 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. Las Vegas: IEEE, 2008: 3465-3468.
|
|
|
[14] |
LASHERMES B, JAFFARD S, ABRY P. Wavelet leader based multifractal analysis [C]// 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing. Philadelphia: IEEE, 2005: 161-164.
|
|
|
[15] |
陈法法, 汤宝平, 苏祖强 基于等距映射与加权KNN的旋转机械故障诊断[J]. 仪器仪表学报, 2013, 34 (1): 215- 220 CHEN Fa-fa, TANG Bao-ping, SU Zu-qiang Rotating machinery fault diagnosis based on isometric mapping and weighted KNN[J]. Journal of Instrument and Meter, 2013, 34 (1): 215- 220
doi: 10.3969/j.issn.0254-3087.2013.01.031
|
|
|
[16] |
计会凤, 徐爱功, 隋达嵬 Dijkstra算法的设计与实现[J]. 辽宁工程技术大学学报: 自然科学版, 2008, 27 (增1): 222- 223 JI Hui-feng, XU Ai-gong, SUI Da-wei Design and implementation of Dijkstra algorithm[J]. Journal of Liaoning Technical University: Natural Science, 2008, 27 (增1): 222- 223
|
|
|
[17] |
周志华.机器学习[M]. 北京: 清华大学出版社, 2016: 226-231.
|
|
|
[18] |
惠康华, 肖柏华, 王春恒 基于自适应近邻参数的局部线性嵌入[J]. 模式识别与人工智能, 2010, 23 (6): 842- 846 HUI Kang-hua, XIAO Bai-hua, WANG Chun-heng Self-regulation of neighborhood parameter for locally linear embedding[J]. Pattern Recognition and Artificial Intelligence, 2010, 23 (6): 842- 846
doi: 10.3969/j.issn.1003-6059.2010.06.015
|
|
|
[19] |
MIRJALILI S, MIRJALILI S M, LEWIS A Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69 (3): 46- 61
|
|
|
[20] |
高岳林, 刘俊梅 一种带有随机变异的动态差分进化算法[J]. 计算机应用, 2009, 29 (10): 2719- 2722 GAO Yue-lin, LIU Jun-mei Dynamic differential evolution algorithm with random mutation[J]. Journal of Computer Applications, 2009, 29 (10): 2719- 2722
|
|
|
[21] |
陈仁祥, 汤宝平, 吕中亮 基于相关系数的EEMD转子振动信号降噪方法[J]. 振动、测试与诊断, 2012, 32 (4): 542- 546 CHEN Ren-xiang, TANG Bao-ping, LV Zhong-liang Ensemble empirical mode decomposition de-noising method based on correlation coefficients for vibration signal of rotor system[J]. Journal of Vibration, Measurement and Diagnosis, 2012, 32 (4): 542- 546
doi: 10.3969/j.issn.1004-6801.2012.04.004
|
|
|
[22] |
王志华, 张建峰 基于EEMD降噪与PNN的齿轮箱齿轮故障诊断[J]. 煤矿机械, 2015, 36 (11): 326- 328 WANG Zhi-hua, ZHANG Jian-feng Fault diagnosis of gearbox gear based on EEMD de-noising and PNN[J]. Coal Mine Machinery, 2015, 36 (11): 326- 328
|
|
|
[23] |
时培明, 梁凯, 赵娜, 等 基于深度学习特征提取和粒子群支持向量机状态识别的齿轮智能故障诊断[J]. 中国机械工程, 2017, 28 (9): 1056- 1061 SHI Pei-ming, LIANG Kai, ZHAO Na, et al Intelligent fault diagnosis for gears based on deep learning feature extraction and particle swarm optimization SVM state identification[J]. China Mechanical Engineering, 2017, 28 (9): 1056- 1061
doi: 10.3969/j.issn.1004-132X.2017.09.009
|
|
|
[24] |
张梅军, 王闯, 陈灏 IMF能量和RBF神经网络相结合在滚动轴承故障诊断中的应用研究[J]. 机械, 2012, 39 (6): 63- 66 ZHANG Mei-jun, WANG Chuang, CHEN Hao The application research on the combination of IMF energy and RBF neural network in rolling bearing fault diagnosis[J]. Mechanics, 2012, 39 (6): 63- 66
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|