• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2019, Vol. 55 ›› Issue (12): 50-57.doi: 10.3901/JME.2019.12.050

• 仪器科学与技术 • 上一篇    下一篇

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基于张量分解的滚动轴承复合故障多通道信号降噪方法研究

胡超凡1,2, 王衍学2   

  1. 1. 桂林电子科技大学机电工程学院 桂林 541004;
    2. 北京建筑大学城市轨道交通车辆服役性能保障重点实验室 北京 100044
  • 收稿日期:2018-06-19 修回日期:2019-03-02 出版日期:2019-06-20 发布日期:2019-06-20
  • 通讯作者: 王衍学(通信作者),男,1980年出生,博士,教授,博士研究生导师。主要研究方向为机械系统动力学建模分析,动态信号处理与特征提取,装备故障诊断与智能维护。E-mail:wyx1999140@126.com
  • 作者简介:胡超凡,男,1992年出生,博士研究生。主要研究方向为动态信号处理与特征提取与机电装备健康监测。E-mail:hcf19921230@163.com
  • 基金资助:
    国家自然科学基金(51875032,51475098,61463010)、广西自然科学杰出青年基金(2016GXNSFFA380008)和桂林电子科技大学研究生教育创新计划(2018YJCXB01)资助项目

Research on Multi-channel Signal Denoising Method for Multiple Faults Diagnosis of Rolling Element Bearings Based on Tensor Factorization

HU Chaofan1,2, WANG Yanxue2   

  1. 1. School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004;
    2. Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044
  • Received:2018-06-19 Revised:2019-03-02 Online:2019-06-20 Published:2019-06-20

摘要: 针对滚动轴承多通道信号同时滤波的问题,提出一种新的基于张量分解的多维降噪技术。该方法在高维空间中将时域信号、频率和通道建模为一个三阶张量模型,首先通过Tucker3分解张量模型,然后通过L曲线准则来选取截断高阶奇异值分解(Truncated HOSVD)的截断参数,根据截断参数求解张量模型,最后根据原张量的组建方式通过逆变换得到新的目标张量。通过滚动轴承复合故障仿真分析对所提出的滚动轴承复合故障检测技术的性能进行了评价,然后应用该方法对轴承试验台采集的振动信号进行降噪分析,试验结果表明该方法在滚动轴承复合故障多维降噪以及特征提取中的有效性和可行性。基于张量分析的多维信号滤波技术将拓宽大数据时代处理异构、多维数据的视野。

关键词: 多通道降噪, 复合故障诊断, 滚动轴承, 截断高阶奇异值分解, 张量分解

Abstract: Aiming at simultaneously multi-channel signal filtering and multiple faults diagnosis of the roller bearings, a novel multi-channel signal denoising technique is proposed based on tensor factorization. The original vibration signals are first formulated as a 3-way tensor via temporal signal, frequency and channel in a high dimensional space. The tensor model is factorized using Tucker-3 decomposition. Then, key parameters used in the truncated HOSVD are obtained according to the L-curve criterion. Finally, tensor model is solved by those selected truncated parameters, while target tensor is reconstructed by inverse transformation. The performance of the developed technique in detecting faults of roller bearings has been thoroughly evaluated through simulation signals. The presented approach is then applied to reduce noise in vibration signal collected on bearing test-rigs. Experimental results showed that the feasibility and validity of the proposed method in multiple faults detection of the bearings. Multidimensional signal filtering based on tensor will broaden the view in dealing with heterogeneous and multiaspect data in an age of big data.

Key words: multi-channel denoising, multiple faults diagnosis, rolling element bearing, tensor factorization, truncated HOSVD

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