Toward Data-Driven Structural Health Monitoring: Application of Machine Learning and Signal Processing to Damage Detection
Toward Data-Driven Structural Health Monitoring: Application of Machine Learning and Signal Processing to Damage Detection
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DOI：
10.1061/(asce)cp.1943-5487.0000258
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发表时间：
2013-11-01
影响因子：
6.9
通讯作者：
Jin, Yuanwei
中科院分区：
文献类型：
--
作者：
Ying, Yujie;Garrett, James H., Jr.;Jin, Yuanwei
介绍了一种基于机器学习和信号处理技术综合应用的多层数据驱动结构健康监测框架。本文重点展示了该框架在环境和操作变化下钢管损伤检测的有效性。该管道安装了能够产生和感知超声波的压电片。通过与管道表面耦合的质量散射体油脂物理模拟损坏。良性变化包括随时间变化的内部气压和环境温度。超声波测量是在三天内进行的，分散器放置在管道的不同位置。波形复杂且难以解释，区分由散射体产生的变化和良性变化产生的变化就更加困难了。从各种信号处理技术中提取365个特征来表征感测数据。然后，使用自适应增强算法开发了自动特征选择方法，以识别最有效的损伤检测特征。根据所选择的特征，基于自适应增强和支持向量机构建了5个机器学习分类器，随机测试的平均准确率为98.5-99.8%，系统测试的平均准确率为84.2-89%。此外，从混淆矩阵和接收器工作特性曲线生成的分类器评估的其他指标也被报道。(C) 2013年美国土木工程师学会。
A multilayer data-driven framework for robust structural health monitoring based on a comprehensive application of machine learning and signal processing techniques is introduced. This paper focuses on demonstrating the effectiveness of the framework for damage detection in a steel pipe under environmental and operational variations. The pipe was instrumented with piezoelectric wafers that can generate and sense ultrasonic waves. Damage was simulated physically by a mass scatterer grease-coupled to the surface of the pipe. Benign variations included variable internal air pressure and ambient temperature over time. Ultrasonic measurements were taken on three different days with the scatterer placed at different locations on the pipe. The wave patterns are complex and difficult to interpret, and it is even more difficult to differentiate the changes produced by the scatterer from the changes produced by benign variations. The sensed data were characterized by 365 features extracted from a variety of signal-processing techniques. Automated feature selection methods were then developed using an adaptive boosting algorithm to identify the most effective features for damage detection. With the selected features, five machine-learning classifiers were formulated based on adaptive boosting and support vector machines and achieved 98.5-99.8% average accuracy during random testing and 84.2-89% average accuracy during systematic testing. In addition, other metrics for classifier evaluation generated from a confusion matrix and from a receiver operating characteristic curve are reported. (C) 2013 American Society of Civil Engineers.