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Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring

机译：基于卷积神经网络的利用多种信息进行结构健康监测的数据异常检测方法

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摘要

Structural health monitoring (SHM) is used worldwide for managing and maintaining civil infrastructures. SHM systems have produced huge amounts of data, but the effective monitoring, mining, and utilization of this data still need in-depth study. SHM data generally includes multiple types of anomalies caused by sensor faults or system malfunctions that can disturb structural analysis and assessment. In the routine data pre-processing, multiple signal processing techniques are required to detect the anomalies, respectively, which is inefficient. The large variations of extracted features from massive SHM data make the data anomaly detection techniques prone to be over-processed or under-processed. Even with expert intervention, the parameter tuning, associated with multiple data preprocessing methods, is still a challenge, making the procedure expensive and inefficient. In addition, one data anomaly detection technique frequently mis-detects other types of anomaly. In this work, we focus on the anomaly detection in the stage of data pre-processing that little work has been done based on the real-world continuous SHM data with multiclass anomalies. We proposed a novel data anomaly detection method based on a convolutional neural network (CNN) that imitates human vision and decision making. First, we split raw time series data into sections, and visualized the data in time and frequency domain, respectively. Then each section's images were stacked as a single dual-channel image and labeled according to graphical features (multi-2D image space expression). Second, a CNN was designed and trained for data anomaly classification, during which the descriptions and representations of the anomalies' features were learned by convolution. To validate our work, we considered the effects of balanced and imbalanced training sets and training ratios on actual acceleration data from an SHM system for a long-span cable-stayed bridge. The results show that our method could detect the multipattern anomalies of SHM data efficiently with high accuracy. The proposed dual-information CNN-based design makes this detection process readily scalable, faster, and more accurate, thereby providing a novel perspective with strong potential for SHM data preprocessing.
机译：结构健康监测（SHM）在世界范围内用于管理和维护民用基础设施。 SHM系统已经产生了大量数据，但是有效监视，挖掘和利用这些数据仍需要深入研究。 SHM数据通常包括由传感器故障或系统故障引起的多种类型的异常，这些异常会干扰结构分析和评估。在常规数据预处理中，需要多种信号处理技术来分别检测异常，这是低效率的。从大量SHM数据中提取的特征的巨大差异使得数据异常检测技术容易被过度处理或处理不足。即使在专家干预下，与多种数据预处理方法关联的参数调整仍然是一个挑战，使过程昂贵且效率低下。另外，一种数据异常检测技术经常误检测其他类型的异常。在这项工作中，我们将重点放在数据预处理阶段的异常检测上，该工作很少基于基于现实世界中具有多类异常的连续SHM数据完成的工作。我们提出了一种基于卷积神经网络（CNN）的新型数据异常检测方法，该方法模仿了人类的视觉和决策能力。首先，我们将原始时间序列数据划分为多个部分，并分别在时域和频域中可视化数据。然后，将每个部分的图像堆叠为单个双通道图像，并根据图形功能（多2D图像空间表达）进行标记。其次，为数据异常分类设计并训练了CNN，在此期间通过卷积学习了异常特征的描述和表示。为了验证我们的工作，我们考虑了平衡和不平衡训练集以及训练比率对大跨度斜拉桥SHM系统实际加速度数据的影响。结果表明，我们的方法可以高效，高效地检测SHM数据的多模式异常。提出的基于CNN的双信息设计使该检测过程易于扩展，更快和更准确，从而为SHM数据预处理提供了具有强大潜力的新颖视角。

著录项

  • 来源
    《Structural Control and Health Monitoring》 |2019年第1期|e2296.1-e2296.22|共22页
  • 作者

    Tang Zhiyi; Chen Zhicheng; Bao Yuequan; Li Hui;

  • 作者单位

    Harbin Inst Technol, Key Lab Struct Dynam Behav & Control, Minist Educ, Harbin, Heilongjiang, Peoples R China|Harbin Inst Technol, Key Lab Intelligent Disaster Mitigat, Minist Ind & Informat Technol, Harbin, Heilongjiang, Peoples R China|Harbin Inst Technol, Sch Civil Engn, Harbin, Heilongjiang, Peoples R China;

    Harbin Inst Technol, Key Lab Struct Dynam Behav & Control, Minist Educ, Harbin, Heilongjiang, Peoples R China|Harbin Inst Technol, Key Lab Intelligent Disaster Mitigat, Minist Ind & Informat Technol, Harbin, Heilongjiang, Peoples R China|Harbin Inst Technol, Sch Civil Engn, Harbin, Heilongjiang, Peoples R China;

    Harbin Inst Technol, Key Lab Struct Dynam Behav & Control, Minist Educ, Harbin, Heilongjiang, Peoples R China|Harbin Inst Technol, Key Lab Intelligent Disaster Mitigat, Minist Ind & Informat Technol, Harbin, Heilongjiang, Peoples R China|Harbin Inst Technol, Sch Civil Engn, Harbin, Heilongjiang, Peoples R China;

    Harbin Inst Technol, Key Lab Struct Dynam Behav & Control, Minist Educ, Harbin, Heilongjiang, Peoples R China|Harbin Inst Technol, Key Lab Intelligent Disaster Mitigat, Minist Ind & Informat Technol, Harbin, Heilongjiang, Peoples R China|Harbin Inst Technol, Sch Civil Engn, Harbin, Heilongjiang, Peoples R China;

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  • 正文语种 eng
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  • 关键词

    computer vision; convolutional neural network (CNN); data anomaly detection; long-span bridge; structural health monitoring (SHM);

    机译：电脑视觉;卷积神经网络（CNN）;数据异常检测;长跨度桥;结构健康监测（SHM）;

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