文章基本信息

标题：Effective fault detection in structural health monitoring systems
本地全文：下载
作者：Marwa Chaabane ; Majdi Mansouri ; Kamaleldin Abodayeh 等
期刊名称：Advances in Mechanical Engineering
印刷版ISSN：1687-8140
电子版ISSN：1687-8140
出版年度：2019
卷号：11
期号：9
页码：1-17
DOI：10.1177/1687814019873234
出版社：Sage Publications Ltd.
摘要：A new fault detection technique is considered in this article. It is based on kernel partial least squares, exponentially weighted moving average, and generalized likelihood ratio test. The developed approach aims to improve monitoring the structural systems. It consists of computing an optimal statistic that merges the current information and the previous one and gives more weight to the most recent information. To improve the performances of the developed kernel partial least squares model even further, multiscale representation of data will be used to develop a multiscale extension of this method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale kernel partial least squares method that combines the advantages of the kernel partial least squares method with those of multiscale representation will be developed to enhance the structural modeling performance. The effectiveness of the proposed approach is assessed using two examples: synthetic data and benchmark structure. The simulation study proves the efficiency of the developed technique over the classical detection approaches in terms of false alarm rate, missed detection rate, and detection speed.
其他关键词：Fault detection ; multiscale kernel partial least squares ; generalized likelihood ratio test ; exponentially weighted moving average ; structural health monitoring