基于核极限学习机自编码器的转盘轴承寿命状态识别

doi:10.3785/j.issn.1008-973X.2022.09.019

浙江大学学报(工学版)

2022, Vol. 56

Issue (9): 1856-1866 DOI: 10.3785/j.issn.1008-973X.2022.09.019

机械工程

基于核极限学习机自编码器的转盘轴承寿命状态识别

潘裕斌1(

),王华1,2,*(

),陈捷1,2,洪荣晶1,2

1. 南京工业大学机械与动力工程学院，江苏南京 211816
2. 江苏省工业装备数字制造及控制技术重点实验室，江苏南京 211816

Life condition recognition of slewing bearing based on kernel extreme learning machine based auto-encoder

Yu-bin PAN1(

),Hua WANG1,2,*(

),Jie CHEN1,2,Rong-jing HONG1,2

1. School 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

全文: PDF(2091 KB) HTML

摘要：

针对低速重载转盘轴承运行工况恶劣、故障特征微弱的特点，提出基于飞蛾扑火算法优化多层核极限学习机自编码器（MFO-MLKELM-AE）的转盘轴承寿命状态识别方法. 该方法从振动信号的时域和时频域中提取出多个能够表征转盘轴承运行状态的特征向量，并将其组成高维特征集. 采用堆叠多层核极限学习机自编码器（MLKELM-AE），从高维特征集中提取最能反映转盘轴承的寿命状态信息，输入核极限学习机（KELM）模型进行寿命状态识别. 在MLKELM-AE学习训练中，采用新的飞蛾扑火算法（MFO）优化惩罚系数和核参数，提高MLKELM-AE的特征识别能力. 转盘轴承加速寿命实验表明，MLKELM-AE比多层极限学习机自编码器（MLELM-AE）、单层极限学习机（ELM）、KELM的识别精度高，多传感器、多领域特征能够全面反映转盘轴承的寿命状态.

关键词： 低速重载转盘轴承; 多层核极限学习机自编码器（MLKELM-AE）; 飞蛾扑火算法（MFO）; 寿命状态识别; 多领域特征

Abstract:

A life condition recognition method based on multi-layer kernel extreme learning machine based auto-encoder optimized by moth-flame optimization (MFO-MLKELM-AE) was proposed to solve the problem of low-speed heavy-load slewing bearing, such as poor working condition and weak fault feature. Firstly, multiple feature vectors were extracted from time domain and time-frequency domain of vibration signal to make a high dimension feature set which can characterize the operation condition of slewing bearing. Secondly, multi-layer kernel extreme learning machine based auto-encoder (MLKELM-AE) was utilized to extract the vectors which best reflect the slewing bearing life condition information from the high dimension feature set. Thirdly, the vectors were inputted into the kernel extreme learning machine (KELM) for the life condition recognition. Finally, a new moth-flame optimization (MFO) was proposed to optimize the penalty coefficient and kernel parameter for the improvement of MLKELM-AE feature recognition ability in the training process. The accelerated life test of slewing bearing shows that the recognition accuracy of MLKELM-AE is better than multi-layer extreme learning machine based auto-encoder (MLELM-AE), single layer extreme learning machine (ELM) and KELM. The multi-sensor and multi-domain features can reflect the life condition of slewing bearing more comprehensively.

Key words: low-speed heavy-load slewing bearing multi-layer kernel extreme learning machine based auto-encoder (MLKELM-AE) moth-flame optimization (MFO) life condition recognition multi-domain features

收稿日期: 2021-10-27 出版日期: 2022-09-28

CLC:

TH 17

基金资助: 国家自然科学基金资助项目（51875273）；中国博士后科学基金资助项目（2021M691558）；江苏省自然科学基金资助项目（BK20210547）；江苏省高等学校自然科学研究项目资助（21KJB460036）；江苏省博士后科研资助计划项目（2021K297B）

通讯作者: 王华 E-mail: panyb@njtech.edu.cn;njtechwh@yeah.net

作者简介: 潘裕斌（1992—），男，博士后，从事旋转机械状态监测研究. orcid.org/0000-0002-7193-7701. E-mail： panyb@njtech.edu.cn

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	作者相关文章
	潘裕斌
	王华
	陈捷
	洪荣晶

引用本文:

潘裕斌,王华,陈捷,洪荣晶. 基于核极限学习机自编码器的转盘轴承寿命状态识别[J]. 浙江大学学报(工学版), 2022, 56(9): 1856-1866.

Yu-bin PAN,Hua WANG,Jie CHEN,Rong-jing HONG. Life condition recognition of slewing bearing based on kernel extreme learning machine based auto-encoder. Journal of ZheJiang University (Engineering Science), 2022, 56(9): 1856-1866.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.09.019 或 https://www.zjujournals.com/eng/CN/Y2022/V56/I9/1856

图 1 核极限学习机自编码器结构示意图

图 2 多层核极限学习机自编码器结构示意图

图 3 转盘轴承寿命状态识别流程图

图 4 转盘轴承综合性能实验台简图

表 1 转盘轴承实验台性能参数

图 5 转盘轴承结构及传感器安装

图 6 转盘轴承实验台实物图

图 7 转盘轴承损伤部件

图 8 不同寿命阶段的振动信号

图 9 全寿命温度、驱动力矩信号

图 10 全寿命振动信号特征趋势曲线

图 11 不同核极限学习机自编码器层数的识别结果

图 12 多层核极限学习机自编码器学习后的特征输出

图 13 不同领域特征下寿命状态识别结果的混淆矩阵

表 2 多领域特征下不同优化算法的寿命识别结果

表 3 不同领域特征下参数优化与未优化的寿命状态识别精度

表 4 多领域特征下不同识别模型的寿命状态识别结果

表 5 不同领域特征下不同识别模型的寿命状态识别精度

表 6 多传感器特征融合下不同识别模型的寿命状态识别精度

1	CERRADA M, SÁNCHEZ R V, LI C, et al A review on data-driven fault severity assessment in rolling bearings[J]. Mechanical Systems and Signal Processing, 2018, 99: 169- 196 doi: 10.1016/j.ymssp.2017.06.012
2	赵祥龙, 陈捷, 洪荣晶, 等基于Wavelet leader和优化的等距映射算法的回转支承自适应特征提取[J]. 浙江大学学报:工学版, 2019, 53 (11): 2092- 2101 ZHAO Xiang-long, CHEN Jie, HONG Rong-jing, et al Adaptive feature extraction method for slewing bearing based on Wavelet leader and optimized isometric mapping method[J]. Journal of Zhejiang University: Engineering Science, 2019, 53 (11): 2092- 2101
3	封杨, 黄筱调, 洪荣晶, 等基于数据驱动的回转支承性能退化评估方法[J]. 中南大学学报:自然科学版, 2017, 48 (3): 684- 693 FENG Yang, HUANG Xiao-diao, HONG Rong-jing, et al A multi-dimensional data-driven method for large-size slewing bearings performance degradation assessment[J]. Journal of Central South University: Science and Technology, 2017, 48 (3): 684- 693
4	NAYANA B R GEETHANJALI P. Analysis of statistical time-domain features effectiveness in identification of bearing faults from vibration signal[J]. IEEE Sensors Journal, 2017, 17 (17): 5618- 5625 doi: 10.1109/JSEN.2017.2727638
5	CAESARENDRA W, KOSASIH P B, TIEU A K, et al Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition[J]. Journal of Mechanical Science and Technology, 2013, 27: 2253- 2262 doi: 10.1007/s12206-013-0608-7
6	LIU Z, ZHANG L, CARRASCO J Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method[J]. Renewable Energy, 2020, 146: 99- 110 doi: 10.1016/j.renene.2019.06.094
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	李媛媛, 陈捷, 黄筱调, 等基于改进模糊C均值的回转支承寿命状态识别[J]. 计算机集成制造系统, 2018, 24 (11): 2751- 2758 LI Yuan-yuan, CHEN Jie, HUANG Xiao-diao, et al Life state recognition of slewing bearing based on improved fuzzy C-means[J]. Computer Integrated Manufacturing Systems, 2018, 24 (11): 2751- 2758 doi: 10.13196/j.cims.2018.11.010
9	付文博, 孙涛, 梁藉, 等深度学习原理及应用综述[J]. 计算机科学, 2018, 45 (6A): 11- 15 FU Wen-bo, SUN Tao, LIANG Ji, at al Review of principle and application of deep learning[J]. Computer Science, 2018, 45 (6A): 11- 15 doi: 10.11896/j.issn.1002-137X.2018.Z6.002
10	KASUN L L C, ZHOU H, HUANG G B Representational learning with ELMs for big data[J]. IEEE Intelligent Systems, 2013, 28 (6): 31- 34
11	YU W, ZHUANG F, HE Q, et al Learning deep representations via extreme learning machines[J]. Neurocomputing, 2015, 149: 308- 315 doi: 10.1016/j.neucom.2014.03.077
12	ZHANG N, DING S, SHI Z Denoising Laplacian multi-layer extreme learning machine[J]. Neurocomputing, 2016, 171: 1066- 1074 doi: 10.1016/j.neucom.2015.07.058
13	SUN K, ZHANG J, ZHANG C, et al Generalized extreme learning machine autoencoder and a new deep neural network[J]. Neurocomputing, 2017, 230: 374- 381 doi: 10.1016/j.neucom.2016.12.027
14	桑发文. 深度核极限学习机和相似性LSSVM时间序列预测模型研究[D]. 兰州: 兰州大学, 2019: 18. Sang Fa-wen. Research on time series prediction model of deep kernel extreme learning machine and LSSVM based on similarity [D]. Lanzhou: Lanzhou University, 2019: 18.
15	邓万宇, 郑庆华, 陈琳, 等神经网络极速学习方法研究[J]. 计算机学报, 2010, 33 (2): 279- 287 DENG Wan-yu, ZHENG Qing-hua, CHEN Lin, et al Research on extreme learning of neural networks[J]. Chinese Journal of Computers, 2010, 33 (2): 279- 287 doi: 10.3724/SP.J.1016.2010.00279
16	HUANG G B An insight into extreme learning machines: Random neurons, random features and kernels[J]. Cognitive Computation, 2014, 6: 376- 390 doi: 10.1007/s12559-014-9255-2
17	商强, 林赐云, 杨兆升, 等基于变量选择和核极限学习机的交通事件检测[J]. 浙江大学学报:工学版, 2017, 51 (7): 1339- 1346 SHANG Qiang, LIN Ci-yun, YANG Zhao-sheng, et al Traffic incident detection based on variable selection and kernel extreme learning machine[J]. Journal of Zhejiang University:Engineering Science, 2017, 51 (7): 1339- 1346
18	胡爱军, 张军华, 刘随贤, 等滚动轴承多工况故障的特征自动选择核极限学习机智能识别方法[J]. 振动与冲击, 2020, 39 (23): 182- 189 HU Ai-jun, ZHANG Jun-hua, LIU Sui-xian, et al Intelligent identification method using kernel extreme learning machine for rolling bearing multi-working condition multi-feature automatic selection[J]. Journal of Vibration and Shock, 2020, 39 (23): 182- 189
19	SCHÖLKOPF B, SMOLA A J. Learning with kernels: support vector machines, regularization and beyond [M]. Cambridge: MIT Press, 2002.
20	MIRJALILI S Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm[J]. Knowledge-Based Systems, 2015, 89: 228- 249 doi: 10.1016/j.knosys.2015.07.006
21	李志明, 莫愿斌, 张森一种新颖的群智能算法: 飞蛾扑火优化算法[J]. 电脑知识与技术, 2016, 12 (31): 172- 176 LI Zhi-ming, MO Yuan-bin, ZHANG Sen A novel swarm intelligence optimization algorithm: moth-flame optimization algorithm[J]. Computer Knowledge and Technology, 2016, 12 (31): 172- 176
22	CHENG Y, ZHAO D, WANG Y, et al Multi-label learning with kernel extreme learning machine autoencoder[J]. Knowledge-Based Systems, 2019, 178: 1- 10 doi: 10.1016/j.knosys.2019.04.002
23	郭建华, 杨海东基于支持向量免疫集成预测的电信网络性能监控[J]. 中南大学学报:自然科学版, 2012, 43 (3): 1020- 1026 GUO Jian-hua, YANG Hai-dong Telecom networks performance monitoring based on artificial immune support vector regression[J]. Journal of Central South University: Science and Technology, 2012, 43 (3): 1020- 1026

[1]	许昱晖,舒俊清,宋亚,郑宇,夏唐斌. 基于多时间尺度相似性的涡扇发动机寿命预测[J]. 浙江大学学报(工学版), 2021, 55(10): 1937-1947.
[2]	张秀芬,高云飞. 退役机械零部件多维递阶再制造性评价方法[J]. 浙江大学学报(工学版), 2020, 54(5): 954-962.
[3]	汪海晋,尹宗宇,柯臻铮,郭英杰,董辉跃. 基于一维卷积神经网络的螺旋铣刀具磨损监测[J]. 浙江大学学报(工学版), 2020, 54(5): 931-939.
[4]	袁幸,朱永生,张优云,洪军,祁文昌. 基于正反问题的滚动轴承损伤程度评估[J]. J4, 2012, 46(11): 1960-1967.
[5]	杨先勇, 周晓军, 林勇, 张文斌, 沈路. 基于V-detector算法的滚动轴承故障诊断方法[J]. J4, 2010, 44(9): 1805-1810.
[6]	杨先勇, 周晓军, 张文斌, 杨富春. 基于局域波法和KPCA-LSSVM的滚动轴承故障诊断[J]. J4, 2010, 44(8): 1519-1524.

Viewed

Full text

Abstract

Cited

Shared

Discussed