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首页> 外文会议>Conference on Nondestructive Evaluation and Health Monitoring of Aerospace Materials and Composites III; 20040316-20040317; San Diego,CA; US >Novel event identification for SHM systems using unsupervised neural computation
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Novel event identification for SHM systems using unsupervised neural computation

机译：使用无监督神经计算的SHM系统新型事件识别

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摘要

This paper explores the use of unsupervised neural networks and frequency sensitive competitive learning for novel event identification in structural health monitoring (SHM) systems. Our approach assigns a novelty metric based upon the output states of an SHM system. The technique can be applied in data decimation schemes, to enhance the monitoring of such systems, and as an aide to SHM data analysis. Learning units provide a means of characterizing an SHM system, and are subsequently used to assign a novelty metric to new SHM data. The system has been evaluated using data from the Taylor Bridge and Golden Boy statue in Winnipeg, Canada and the Portage Creek bridge in Victoria, Canada. The system is capable of analyzing SHM data from a 14-channel system, recording data at 32 Hz, using 32 learning units at approximately 30 times real-time on an AMD AthlonXP 2500+ based computer. The event identification system is most sensitive to SHM data which exhibits unusual power spectra, including data which shows abrupt changes in sensor outputs. The system may be cascaded in order to perform basic classification of events after identification.
机译：本文探讨了无监督神经网络和频率敏感竞争学习在结构健康监测（SHM）系统中用于新颖事件识别的用途。我们的方法基于SHM系统的输出状态分配新颖性度量。该技术可以应用在数据抽取方案中，以增强对此类系统的监视，并可以作为SHM数据分析的助手。学习单元提供了一种表征SHM系统的方法，随后用于为新的SHM数据分配新奇性度量。该系统已使用来自加拿大温尼伯的泰勒大桥和金童雕像以及加拿大维多利亚的Portage Creek大桥的数据进行了评估。该系统能够分析基于14通道系统的SHM数据，在基于AMD AthlonXP 2500+的计算机上使用32个学习单元以大约30倍的实时性记录32 Hz的数据。事件识别系统对显示异常功率谱的SHM数据最为敏感，包括显示传感器输出突变的数据。该系统可以被级联以便在识别之后执行事件的基本分类。

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