Modified self-organising map for automated novelty detection applied to vibration signal monitoring
Modified self-organising map for automated novelty detection applied to vibration signal monitoring
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DOI：
10.1016/j.ymssp.2005.01.008
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发表时间：
2006-04
影响因子：
8.4
通讯作者：
M. Wong;L. B. Jack;A. Nandi
中科院分区：
文献类型：
--
作者：
M. Wong;L. B. Jack;A. Nandi
提出了一种基于振动信号检测的机器状态监测方法和一种基于功率谱密度高阶统计量的特征提取方法。这种新的MCM方法是基于Kohonen的自组织映射，并采用了一个多维相异性双类分类措施。该方法被设计为高度模块化，并可扩展为多传感器状态监测环境。使用多达八个传感器的真实振动数据集进行的实验表明，在不同的状态监测应用中，分类精度和鲁棒性都很高。
This paper proposes a novelty detection-based method for machine condition monitoring (MCM) using vibration signals and a new feature extraction method based on higher-order statistics of the power spectral density. This novel MCM method is based on Kohonen's self-organising map and adopts a multidimensional dissimilarity measure for dual class classification. The approach is designed to be highly modular and scale well for a multi-sensor condition monitoring environment. Experiments using real-world vibration data sets with upto eight sensors have shown high accuracy in classification and robustness across different condition monitoring applications.