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首页> 外文期刊>Complexity >A Novel Fault Diagnosis Method for Rolling Bearing Based on Improved Sparse Regularization via Convex Optimization
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A Novel Fault Diagnosis Method for Rolling Bearing Based on Improved Sparse Regularization via Convex Optimization

机译：基于通过凸优化改进的稀疏正则化的滚动轴承的一种新型故障诊断方法

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摘要

Structural health monitoring and fault state identification of key components, such as rolling bearing, located in the mechanical main drive system, have a vital significance. The acquired fault signal of rolling bearing always presents the obvious nonlinear and nonstationary characteristics. Moreover, the concerned features are submerged in strong background noise. To handle this difficulty, a novel fault signal denoising scheme based on improved sparse regularization via convex optimization is proposed to extract the fault feature of rolling bearing. In this paper, the generalized minimax-concave (GMC) penalty is firstly researched to promote the sparsity of signal, which is based on traditional L-1-norm and Huber function. It is designed to estimate the sparse solutions more accurately and maintain the convexity of the cost function. Then, the GMC penalty is extended to 1-D first-order total variation (TV) as nonseparability and nonconvex regularizer. Thus, a convex optimization problem, which involves a quadratic data fidelity term and a convex regularization term, is developed in this paper. To accelerate the convergence of the algorithm, it is solved by forward-backward (FB) iterative algorithm and thus the denoised signal can be obtained. In order to demonstrate its performance, the proposed method is illustrated for numerical simulation signal and applied in the feature extraction of the measured rolling bearing vibration signal.
机译：结构健康监测和故障状态识别关键部件，如滚动轴承，位于机械主驱动系统中，具有至关重要的意义。滚动轴承的获取故障信号始终呈现明显的非线性和非间断特性。此外，有关的特征在强大的背景噪声中浸没。为了处理这种困难，提出了一种基于通过凸优化改进的稀疏正则化的新型故障信号去噪方案，以提取滚动轴承的故障特征。在本文中，首先研究了广义的Minimax-yourave（GMC）惩罚，以促进信号的稀疏性，这是基于传统的L-1-NOM和HUBER功能。它旨在更准确地估计稀疏解决方案，并保持成本函数的凸起。然后，GMC罚款扩展到1-D一阶总变量（电视）作为不可脱离性和非透露规范器。因此，在本文中开发了涉及二次数据保真度术语和凸正则化术语的凸优化问题。为了加速算法的收敛，它通过前后（FB）迭代算法来解决，因此可以获得去噪信号。为了展示其性能，所提出的方法被示出用于数值模拟信号，并施加在测量的滚动轴承振动信号的特征提取中。

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  • 来源
    《Complexity》 |2018年第2期|共10页
  • 作者

    Zhong Dongjie; Yi Cancan; Xiao Han; Zhang Houzhuang; Wu Anding;

  • 作者单位

    Wuhan Univ Sci &

    Technol Key Lab Met Equipment &

    Control Technol Minist Educ Wuhan 430081 Hubei Peoples R China;

    Wuhan Univ Sci &

    Technol Key Lab Met Equipment &

    Control Technol Minist Educ Wuhan 430081 Hubei Peoples R China;

    Wuhan Univ Sci &

    Technol Key Lab Met Equipment &

    Control Technol Minist Educ Wuhan 430081 Hubei Peoples R China;

    Wuhan Univ Sci &

    Technol Key Lab Met Equipment &

    Control Technol Minist Educ Wuhan 430081 Hubei Peoples R China;

    Wenzhou Special Equipment Inspect &

    Res Inst Wenzhou 325007 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 大系统理论;
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