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首页> 外文期刊>Complexity >Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery
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Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

机译：基于稀疏表示自学习词典的快速谱相关及其在旋转机械故障诊断中的应用

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摘要

Rolling element bearing and gear are the typical supporting or rotating parts in mechanical equipment, and it has important economy and security to realize their quick and accurate fault detection. As one kind of powerful cyclostationarity signal analyzing method, spectral correlation (SC) could identify the impulsive characteristic component buried in the vibration signals of rotating machinery effectively. However, the fault feature such as impulsive characteristic component is often interfered by other background noise, and the situation is serious especially in early weak fault stage. Besides, the traditional SC method has a drawback of low computation efficiency which hinders its wide application to some extent. To address the above problems, an impulsive feature-enhanced method which combines fast spectral correlation (FSC) with sparse representation self-learning dictionary is proposed in the paper. Firstly, the sparse representation self-learning dictionary method-K-means singular value decomposition (KSVD) is improved and the improved KSVD (IKSVD) method is used to denoise the original signal, and the periodic impulses are highlighted. Then, the FSC algorithm is applied on the denoised signal and spectral correlation image could be obtained. Finally, the calculated enhanced envelope spectrum (EES) of the denoised signal is obtained by using the spectral correlation image to identify the accurate fault position. The feasibility and superiority of the proposed method is verified through simulation, experiment, and engineering application.
机译：滚动元件轴承和齿轮是机械设备中的典型支撑或旋转部件，具有重要的经济和安全性，实现了快速准确的故障检测。作为一种强大的循环棘轮性信号分析方法，光谱相关（SC）可以有效地识别旋转机械振动信号中掩埋的脉冲特性分量。然而，诸如冲动特征组分的故障特征通常由其他背景噪声干扰，并且情况严重尤其在早期弱故障阶段。此外，传统的SC方法具有低计算效率的缺点，在一定程度上阻碍了广泛的应用。为了解决上述问题，在纸上提出了一种与稀疏表示自学习词典的快速谱相关（FSC）结合快速谱相关（FSC）的脉冲特征增强方法。首先，改进了稀疏表示自学习字典方法-K-均值奇异值分解（KSVD），并且改进的KSVD（IKSVD）方法用于代位于原始信号，并且突出显示周期性的脉冲。然后，在去噪信号上施加FSC算法，并且可以获得光谱相关图像。最后，通过使用光谱相关图像来识别准确的故障位置来获得所计算的增强信号的增强型包络谱（EES）。通过模拟，实验和工程应用验证了所提出的方法的可行性和优越性。

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