Fault detection in non-Gaussian vibration systems using dynamic statistical-based approaches
Fault detection in non-Gaussian vibration systems using dynamic statistical-based approaches
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DOI：
10.1016/j.ymssp.2010.03.015
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发表时间：
2010-11
影响因子：
8.4
通讯作者：
Zhiqiang Ge;U. Kruger;Lisa Lamont;Lei Xie;Zhihuan Song
中科院分区：
文献类型：
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作者：
Zhiqiang Ge;U. Kruger;Lisa Lamont;Lei Xie;Zhihuan Song
本文开发并对比了两种不同的基于几何的技术，用于监测产生随机、非高斯和相关振动信号的机械系统。该领域的现有工作依赖于这样的假设，即记录的信号遵循多正态分布和/或数据模型是静态的，即假设信号不具有串行相关性。所开发的方法依赖于（i）最近的工作独立成分分析和支持向量数据描述，适用于动态数据结构和（ii）纳入统计本地的方法到一个动态的数据表示。来自齿轮箱系统的实验数据的分析证实：（i）这些信号内和之间的显著的自相关和互相关，以及（ii）它们不能被假设为遵循高斯分布。这两种方法的应用表明，他们是更敏感的早期故障比传统的多元统计方法。
This article develops and contrasts two different statistical-based techniques for monitoring mechanical systems that produce stochastic, non-Gaussian, and correlated vibration signals. Existing work in this area relies on the assumption that the recorded signals follow a multinormal distribution and/or the data model is static, i.e. the signals are assumed to possess no serial correlation. The developed approaches rely on (i) recent work on independent component analysis and support vector data description that is applied to a dynamic data structure and (ii) the incorporation of the statistical local approach into a dynamic data representation. The analysis of experimental data from a gearbox system confirms (i) significant auto- and cross-correlation within and among these signals and (ii) that they cannot be assumed to follow Gaussian distributions. The application of both approaches showed that they are more sensitive to incipient faults than conventional multivariate statistical methods.