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Sparse signal recovery based on adaptive algorithms for debris detector

机译：基于碎片检测器的自适应算法的稀疏信号恢复

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摘要

Inductive debris sensors are generally used in online debris detection and perform well in monitoring the wear condition of rotating facilities. The detection accuracy is restricted by the superposition and noise of the impedance signal. From the perspective of machine learning, superposition is underfitting and noise is overfitting, and both are caused by inappropriate model complexity. Therefore, a series of machine learning approaches is proposed in this paper to devise a sparse signal processing model that can adapt to model complexity. We propose that the algorithm applied to debris detection can recover the impulse signals from the impedance signals and help determine the material and the size of the debris. Numerical simulations are carried out to prove that the superposition resolution is 0.6 half-wave width and has the ability to resist noise with a signal-to-noise ratio of more than 10 dB. With the test results, we demonstrate how the proposed method can solve superposition and resist noise.
机译：电感碎片传感器通常用于在线碎片检测，在监测旋转设施的磨损条件时表现良好。检测精度受阻抗信号的叠加和噪声的限制。从机器学习的角度来看，叠加刚刚磨损，噪音已经过度装备，两者都是由不适当的模型复杂性引起的。因此，在本文中提出了一系列机器学习方法，以设计一种可以适应模型复杂性的稀疏信号处理模型。我们建议应用于碎屑检测的算法可以从阻抗信号中恢复脉冲信号，并有助于确定碎片的材料和尺寸。进行数值模拟以证明叠加分辨率是0.6的半波宽，并且具有抵抗具有大于10dB的信噪比的噪声的能力。通过测试结果，我们展示了所提出的方法如何解决叠加和抗蚀噪声。

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