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SOSO Boosting of the K-SVD Denoising Algorithm for Enhancing Fault-Induced Impulse Responses of Rolling Element Bearings

机译:K-SVD去噪算法的SOSO增强,可增强滚动轴承故障引起的脉冲响应

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摘要

The key to successful detection of a localized bearing fault lies in extracting fault-induced impulse responses. Unfortunately, these responses are often contaminated by background noise. The popular K-singular value decomposition (K-SVD) denoising algorithm can be used to extract impulse responses from noise, but it may obtain weak impulse responses against reliable fault detection. An SOSO boosting technique is proposed to improve the performance of the K-SVD denoising algorithm. Given an initial denoised signal, this paper proposes iteratively repeating the following SOSO boosting procedure: 1) Strengthen the underlying signal by adding the previous denoised signal to the noisy input signal; 2) Operate the K-SVD denoising algorithm on the strengthened signal; 3) Subtract the previous denoised signal from the restored signal-strengthened outcome; and 4) Operate the modified K-SVD denoising algorithm again on the resulting signal after the subtraction. The last step, i.e., the secondary denoising step, plays a crucial role in preventing the enhancement of the residual noise that originates from the first denoised result. The results of numerical and experimental studies show that the SOSO-based K-SVD denoising algorithm not only enhances weak impulse responses significantly but also reduces potential residual noise effectively compared to the SOS based and the original K-SVD denoising algorithms.
机译:成功检测局部轴承故障的关键在于提取故障引起的脉冲响应。不幸的是,这些响应经常被背景噪声污染。流行的K奇异值分解(K-SVD)去噪算法可用于从噪声中提取脉冲响应,但对于可靠的故障检测,它可能会获得较弱的脉冲响应。提出了一种SOSO提升技术来提高K-SVD去噪算法的性能。给定初始去噪信号,本文提出迭代地重复以下SOSO增强过程:1)通过将先前的去噪信号添加到有噪声的输入信号来增强基础信号; 2)对增强后的信号进行K-SVD去噪算法; 3)从恢复的信号增强结果中减去先前的降噪信号; 4)对相减后的结果信号再次运行改进的K-SVD去噪算法。最后一步,即二次去噪步骤,在防止增强源自第一次去噪结果的残留噪声方面起着至关重要的作用。数值和实验研究的结果表明,与基于SOS的K-SVD和原始K-SVD降噪算法相比,基于SOSO的K-SVD降噪算法不仅显着增强了弱脉冲响应,而且还有效地降低了潜在的残留噪声。

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