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首页> 外文期刊>Mechanical systems and signal processing >Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis
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Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis

机译：时频原子驱动的支持向量机在轴承早期故障诊断中的应用

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摘要

Bearing plays an essential role in the performance of mechanical system and fault diagnosis of mechanical system is inseparably related to the diagnosis of the bearings. However, it is a challenge to detect weak fault from the complex and non-stationary vibration signals with a large amount of noise, especially at the early stage. To improve the anti-noise ability and detect incipient fault a novel fault detection method based on a short-time matching method and Support Vector Machine (SVM) is proposed. In this paper, the mechanism of roller bearing is discussed and the impact time frequency dictionary is constructed targeting the multi-component characteristics and fault feature of roller bearing fault vibration signals. Then, a short-time matching method is described and the simulation results show the excellent feature extraction effects in extremely low signal-to-noise ratio (SNR). After extracting the most relevance atoms as features, SVM was trained for fault recognition. Finally, the practical bearing experiments indicate that the proposed method is more effective and efficient than the traditional methods in weak impact signal oscillatory characters extraction and incipient fault diagnosis.
机译：轴承在机械系统的性能中起着至关重要的作用，而机械系统的故障诊断与轴承的诊断密不可分。然而，从复杂且不稳定的振动信号中检测到弱故障是一个挑战，该信号带有大量噪声，尤其是在早期。为了提高抗噪能力，检测早期故障，提出了一种基于短时匹配和支持向量机的故障检测方法。本文讨论了滚动轴承的机理，针对滚动轴承故障振动信号的多分量特征和故障特征，建立了冲击时间频率字典。然后，描述了一种短时匹配方法，仿真结果显示出在极低的信噪比（SNR）下出色的特征提取效果。在提取出最相关的原子作为特征之后，对SVM进行了故障识别训练。最后，实际的轴承实验表明，该方法在弱冲击信号振荡特征提取和早期故障诊断中比传统方法更加有效。

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  • 来源
    《Mechanical systems and signal processing》 |2016年第6期|345-370|共26页
  • 作者

    Ruonan Liu; Boyuan Yang; Xiaoli Zhang; Shibin Wang; Xuefeng Chen;

  • 作者单位

    The State Key Laboratory for Manufacturing Systems Engineering, Xi'an, PR China,School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, PR China;

    The State Key Laboratory for Manufacturing Systems Engineering, Xi'an, PR China,School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, PR China;

    The State Key Laboratory for Manufacturing Systems Engineering, Xi'an, PR China,School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, PR China;

    The State Key Laboratory for Manufacturing Systems Engineering, Xi'an, PR China,School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, PR China;

    The State Key Laboratory for Manufacturing Systems Engineering, Xi'an, PR China,School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, PR China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Bearing; Fault diagnosis; Short-time matching; Support vector machine (SVM); Weak signal detection;

    机译：轴承;故障诊断;短时匹配;支持向量机（SVM）;弱信号检测;

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