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Detection of signal transients using independent component analysis and its application in gearbox condition monitoring

机译：使用独立分量分析检测信号瞬变及其在变速箱状态监测中的应用

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摘要

This paper addresses feature extraction of the higher-order statistics, which can effectively characterize the transients, using independent component analysis (ICA) for the one-dimensional measured vibration signal, and then proposes a novel automatic technique for detecting the transients in vibration signals with the low signal-to-noise ratio by ICA feature extraction. The basic principle of the ICA-based transient detection method is that the independent components (ICs) coefficients of the transients and the noise can be effectively distinguished by their different sparseness properties. Specifically, the proposed method mainly includes three steps: training the ICA basis features from the signal segments, denoising the sparse ICs coefficients using the shrinkage function deduced by the maximum a posteriori (MAP) estimation, and reconstructing the transient segments by the shrunken coefficients through the ICA basis functions. Experimental results through the simulated signal analysis and the vibration signal analysis show that the ICA-based method is very effective for transient detection outperforming the traditional methods and is valuable for gearbox condition monitoring and fault diagnosis.
机译：本文针对一维测量的振动信号使用独立分量分析（ICA），解决了高阶统计量的特征提取问题，该特征提取可以有效地表征瞬态，然后提出了一种新的自动技术来检测振动信号中的瞬态。通过ICA特征提取实现低信噪比。基于ICA的瞬态检测方法的基本原理是，瞬态和噪声的独立分量（ICs）系数可以通过其不同的稀疏特性来有效区分。具体地，所提出的方法主要包括三个步骤：训练来自信号段的ICA基本特征；使用由最大后验（MAP）估计推导的收缩函数对稀疏IC系数进行去噪；以及通过收缩系数重构瞬态段，方法是： ICA基函数。通过仿真信号分析和振动信号分析的实验结果表明，基于ICA的方法在瞬态检测方面优于传统方法，对变速箱状态监测和故障诊断具有重要意义。

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