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Enhanced Orthogonal Matching Pursuit Algorithm and Its Application in Mechanical Equipment Fault Diagnosis

机译：改进的正交匹配追踪算法及其在机械设备故障诊断中的应用

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摘要

The vibration signal measured from the mechanical equipment is associated with the operation of key structure, such as the rolling bearing and gear. The effective signal processing method for early weak fault has attracted much attention and it is of vital importance in mechanical fault monitoring and diagnosis. The recently proposed atomic sparse decomposition algorithm is performed around overcomplete dictionary instead of the traditional signal analysis method using orthogonal basis operator. This algorithm has been proved to be effective in extracting useful components from complex signal by reducing influence of background noises. In this paper, an improved linear frequency-modulated (Ilfm) function as an atomis employed in the proposed enhanced orthogonal matching pursuit (EOMP) algorithm. Then, quantum genetic algorithm (QGA) with the OMP algorithm is integrated since the QGA can quickly obtain the global optimal solution of multiple parameters for rapidly and accurately extracting fault characteristic information from the vibration signal. The proposed method in this paper is superior to the traditional OMP algorithm in terms of accuracy and reducing the computation time through analyzing the simulation data and real world data. The experimental results based on the application of gear and bearing fault diagnosis indicate that it is more effective than traditional method in extracting fault characteristic information.
机译：从机械设备测得的振动信号与关键结构（例如滚动轴承和齿轮）的操作相关。有效的早期弱故障信号处理方法引起了广泛关注，在机械故障的监测和诊断中具有至关重要的意义。最近提出的原子稀疏分解算法是围绕超完备字典执行的，而不是使用正交基算子的传统信号分析方法。实践证明，该算法可有效降低背景噪声的影响，从而从复杂信号中提取有用成分。在本文中，一种改进的线性调频（Ilfm）函数作为原子被用于提出的增强型正交匹配追踪（EOMP）算法中。然后，将量子遗传算法（QGA）与OMP算法集成在一起，因为QGA可以快速获得多个参数的全局最优解，以便快速，准确地从振动信号中提取故障特征信息。通过分析仿真数据和真实数据，本文提出的方法在准确性和减少计算时间方面优于传统的OMP算法。基于齿轮和轴承故障诊断应用的实验结果表明，该方法在提取故障特征信息方面比传统方法更为有效。

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  • 来源
    《Shock and vibration》 |2017年第5期|391-403|共13页
  • 作者

    Lv Yong; Luo Jie; Yi Cancan;

  • 作者单位

    Wuhan Univ Sci & Technol, Key Lab Met Equipment & Control, Educ Minist, Wuhan 430081, Hubei, Peoples R China;

    Wuhan Univ Sci & Technol, Key Lab Met Equipment & Control, Educ Minist, Wuhan 430081, Hubei, Peoples R China;

    Wuhan Univ Sci & Technol, Key Lab Met Equipment & Control, Educ Minist, Wuhan 430081, Hubei, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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