计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 258-261.doi: 10.11896/jsjkx.200700039

• 人工智能 • 上一篇    下一篇

基于Gabor小波变换和多核支持向量机的电梯导靴故障诊断方法

朱晓玲1, 李琨1, 张长胜1, 杜付鑫2   

  1. 1 昆明理工大学信息工程与自动化学院 昆明 650504
    2 山东大学机械工程学院 济南 250061
  • 收稿日期:2020-07-07 修回日期:2020-09-18 发布日期:2020-12-17
  • 通讯作者: 李琨(ghfighter@163.com)
  • 作者简介:1210392753@qq.com
  • 基金资助:
    国家自然科学基金(51705289)

Elevator Boot Fault Diagnosis Method Based on Gabor Wavelet Transform and Multi-coreSupport Vector Machine

ZHU Xiao-ling1, LI Kun1, ZHANG Chang-sheng1, DU Fu-xin2   

  1. 1 Faculty of Information Engineering and AutomationKunming University of Science and Technology Kunming 650504,China
    2 School of Mechanical Engineering Shandong UniversityJinan 250061,China
  • Received:2020-07-07 Revised:2020-09-18 Published:2020-12-17
  • About author:ZHU Xiao-ling,born in 1996postgra-duate.Her main research interests include lift fault diagnosis and so on.
    LI Kun,born in 1970Ph.Dassociate professorpostgraduate tutor.His main research interests include elevator fault diagnosiscontrol theory and control engineering.
  • Supported by:
    National Natural Science Foundation(51705289).

摘要: 电梯导靴作为电梯轿厢的重要组成部分对电梯的安全问题具有直接的影响.为了对电梯导靴故障进行更加准确的综合诊断提出了一种基于Gabor小波变换和多核支持向量机的诊断方法.首先通过加速度传感器采集设备主体的振动信号并利用经验模态分解得到固有模态函数分量.然后采用Gabor滤波器对低频分量进行滤波去噪以使提取低频率上数据的特征.最后采用权重的方式将局部和全局的核函数进行线性相加组成多核支持向量机对数据进行分类.实验结果验证了所提方法的有效性相比基于小波变换-最小二乘支持向量机的故障诊断方法所提方法的故障诊断准确率提高了约5%达到了87.6%.

关键词: Gabor小波, 电梯导靴, 多核支持向量机, 故障诊断, 经验模态分解

Abstract: As an important part of the elevator carthe elevator boot has a direct impact on the safety of the elevator.In order to make a more accurate comprehensive diagnosis of the elevator boot failurea diagnosis method based on Gabor wavelet transform and multi-core support vector machine is proposed.Firstthe vibration signal of the main body of the device is collected by an acceleration sensorand the eigenmode function component is obtained by empirical mode decomposition.Thena Gabor filter is used to filter and denoise the low frequency components to achieve the feature enhancement of the extracted data at low frequencies.Finallythe local and global kernel functions are linearly added using weights to form a multi-core support vector machine to classify the data.Experimental results verify the effectiveness of the proposed method.Compared with the fault diagnosis method based on wavelet transform and least squares support vector machinethe fault diagnosis accuracy of the proposed method is improved by about 5%reaching 87.6%.

Key words: Elevator boot, Empirical mode decomposition, Fault diagnosis, Gabor wavelet, Multi-core support vector machine

中图分类号: 

  • TU857
[1] MASOTTI B,MORELLI M.Development of the acceleratedstress testing process at Otis elevator company[J].Quality &Reliability Engineering International,2015,14(6):381-384.
[2] YU L P,LI Y F,ZHU S X.Anomaly detection algorithm based on high-dimensional data stream[J].Computer Engineering,2018,44(1):51-55.
[3] FENG W Z,CAO S Q,ZHAO F.Resonance failure sensitivity for elevator system[J].Journal of Vibration &Shock,2015,34(1):165-170.
[4] LI Y P,SUN L L,ZHANG W.Comparison of augmented and nonaugmented modified Knapp procedure for the treatment of nonrestrictive double-elevator palsies[J].Journal of American Association for Pediatric Ophthalmology &Strabismus,2016,20(5):401-404.
[5] SITI N A,ASMONE A S,CHEW M Y L.An assessment of main-tainability of elevator system to improve facilities management knowledge-base[J].IOP Conference Series Earth and Environmental Science,2018,117(1):12-25.
[6] ERTUGRUL D,HAKAN A Y.Experimental study of the tribological properties of an elevator's brake linings[J].Industrial Lubrication &Tribology,2016,68(6):683-688.
[7] CHEN J,CHENG L,YU H,et al.Health status assessment for complex systems based on EMD-SVD and Mahalanobis-Taguchi system[J].Systems Engineering and Electronics,2017,39(7):1542-1548.
[8] LIU S Z,CHEN Z X.Singular value decomposition and EEMD non-linear vibration signal denoising method[J].Journal of Detection and Control,2019,41(3):37-42.
[9] DENG F H.Research on fault diagnosis method of elevator boot based on CEEMD-TQWT and PSO-LSSVM[D].Kunming:Kunming University of Science and Technology ,2018.
[10] LI Y Q,ZHANG S,LI H B,et al.Face Recognition MethodUsing Gabor Wavelet and Cross-covariance Dimensionality Reduction[J].Journal of Electronics &Information Technology ,2017,39(8):2023-2027.
[11] MALATHI T,BHUYAN M K.Performance analysis of Gabor wavelet for extracting most informative and efficient features[J].Multimedia Tools &Applications,2016,76(6):1-21.
[12] XIANG Y M,WANG F,WAN L,et al.An Advanced Multiscale Edge Detector Based on Gabor Filters for SAR Imagery[J].IEEE Geoscience &Remote Sensing Letters,2017,14(9):1522-1526.
[13] FENG P,QIN D,JI P.Multi-label learning algorithm with SVM based association[J].High Technology Letters,2019,25(1):97-104.
[14] YIN Y,XU D,WANG X,et al.Online State-Based Structured SVM Combined With Incremental PCA for Robust Visual Tracking[J].IEEE Transactions on Cybernetics,2017,45(9):1988-2000.
[15] SILVA M F M,LEIJOTO L F,NOBRE C N.Algorithms Ana-lysis in Adjusting the SVM Parameters:An Approach in the Prediction of Protein Function[J].Applied Artificial Intelligence,2017,31(4):316-331.
[16] ZHANG S,WANG Y,LIU M,et al.Data-based Line TripFault Prediction in Power Systems Using LSTM Networks and SVM[J].IEEE Access,2017:7675-7686.
[17] PHU V,CHAU V T N,TRAN V T N.SVM for English semantic classification in parallel environment[J].International Journal of Speech Technology ,2017,20(3):487-508.
[18] DHAR S,CHERKASSKY V.Development and Evaluation ofCost-Sensitive Universum-SVM[J].IEEE Transactions on Cybernetics,2017,45(4):806-818.
[1] 雷剑梅, 曾令秋, 牟洁, 陈立东, 王淙, 柴勇.
基于整车EMC标准测试和机器学习的反向诊断方法
Reverse Diagnostic Method Based on Vehicle EMC Standard Test and Machine Learning
计算机科学, 2021, 48(6): 190-195. https://doi.org/10.11896/jsjkx.200700204
[2] 王焘, 张树东, 李安, 邵亚茹, 张文博.
一种面向异常传播的微服务故障诊断方法
Anomaly Propagation Based Fault Diagnosis for Microservices
计算机科学, 2021, 48(12): 8-16. https://doi.org/10.11896/jsjkx.210100149
[3] 张宁, 方靖雯, 赵雨宣.
基于LSTM混合模型的比特币价格预测
Bitcoin Price Forecast Based on Mixed LSTM Model
计算机科学, 2021, 48(11A): 39-45. https://doi.org/10.11896/jsjkx.210600124
[4] 朱莹,夏亦犁,裴文江.
基于改进的BEMD的红外与可见光图像融合方法
Fusion of Infrared and Color Visible Images Based on Improved BEMD
计算机科学, 2020, 47(3): 124-129. https://doi.org/10.11896/jsjkx.190100038
[5] 林毅, 吉鸿江, 韩佳佳, 张德平.
一种基于马氏距离的系统故障诊断方法
System Fault Diagnosis Method Based on Mahalanobis Distance Metric
计算机科学, 2020, 47(11A): 57-63. https://doi.org/10.11896/jsjkx.190900174
[6] 姚立霜, 刘丹, 裴作飞, 王云锋.
基于EMD聚类的实时网络流量预测模型
Real-time Network Traffic Prediction Model Based on EMD and Clustering
计算机科学, 2020, 47(11A): 316-320. https://doi.org/10.11896/jsjkx.200100085
[7] 张永安, 颜斌斌.
一种股票市场的深度学习复合预测模型
Deep Learning Hybrid Forecasting Model for Stock Market
计算机科学, 2020, 47(11): 255-267. https://doi.org/10.11896/jsjkx.200500119
[8] 郭杨, 梁家荣, 刘峰, 谢敏.
一种基于超立方体网络的高效故障诊断并行算法
Novel Fault Diagnosis Parallel Algorithm for Hypercube Networks
计算机科学, 2019, 46(5): 73-76. https://doi.org/10.11896/j.issn.1002-137X.2019.05.011
[9] 赵博, 张华峰, 张驯, 赵金雄, 孙碧颖, 袁晖.
基于EMD的电厂网络流量异常检测方法
EMD-based Anomaly Detection for Network Traffic in Power Plants
计算机科学, 2019, 46(11A): 464-468.
[10] 刘佩, 贾建, 陈莉, 安影.
基于快速自适应的二维经验模态分解的图像去噪算法
Image Denoising Algorithm Based on Fast and Adaptive Bidimensional Empirical Mode Decomposition
计算机科学, 2019, 46(11): 260-266. https://doi.org/10.11896/jsjkx.190400159
[11] 王岩, 罗倩, 邓辉.
基于变分贝叶斯的轴承故障诊断方法
Bearing Fault Diagnosis Method Based on Variational Bayes
计算机科学, 2019, 46(11): 323-327. https://doi.org/10.11896/jsjkx.180901719
[12] 张刚, 高俊鹏, 李红威.
级联三稳态随机共振的特性研究及应用
Research on Stochastic Resonance Characteristics of Cascaded Three-steady-state and Its Application
计算机科学, 2018, 45(9): 146-151. https://doi.org/10.11896/j.issn.1002-137X.2018.09.023
[13] 李栋, 薛惠锋.
基于混合模型的中长期降水量预测
Forecasting of Medium and Long Term Precipitation Based on Hybrid Model
计算机科学, 2018, 45(9): 271-278. https://doi.org/10.11896/j.issn.1002-137X.2018.09.045
[14] 张斌,滕俊杰,满毅.
改进的并行fp-growth算法在工业设备故障诊断中的应用研究
Application Research of Improved Parallel Fp-growth Algorithm in Fault Diagnosis
of Industrial Equipment
计算机科学, 2018, 45(6A): 508-512.
[15] 薛善良,杨佩茹,周奚.
基于模糊神经网络的WSN无线数据收发单元故障诊断
WSN Wireless Data Transceiver Unit Fault Diagnosis with Fuzzy Neural Network
计算机科学, 2018, 45(5): 38-43. https://doi.org/10.11896/j.issn.1002-137X.2018.05.006
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!