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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数据异常检测方法，用于结构健康监测的多种信息

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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：全球结构健康监测（SHM）用于管理和维护民事基础设施。 SHM系统产生了大量数据，但有效的监测，挖掘和利用这些数据仍然需要深入研究。 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，这可能会干扰结构分析和评估。在常规数据预处理中，需要多个信号处理技术来分别检测异常，其效率低下。大量SHM数据的提取特征的大变化使得数据异常检测技术容易过度处理或未处理。即使具有专家干预，参数调整，与多个数据预处理方法相关，仍然是一个挑战，使得程序昂贵且效率低下。此外，一种数据异常检测技术经常误导其他类型的异常。在这项工作中，我们专注于数据预处理阶段的异常检测，即根据现实世界的连续SHM数据与多种多组异常进行了一点工作。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，其模仿人体视觉和决策。首先，我们将原始时间序列数据分开到部分中，并分别在时间和频域中可视化数据。然后，每个部分的图像被堆叠为单个双通道图像并根据图形特征标记（多2D图像空间表达式）。其次，为数据异常分类设计和培训了CNN，在此期间，通过卷积学习了异常的特征的描述和表示。为了验证我们的工作，我们考虑了平衡和不平衡培训群和培训比对长跨度斜拉桥SHM系统的实际加速数据的影响。结果表明，我们的方法可以高精度地检测SHM数据的多模式异常。所提出的基于双信息基于CNN的设计使得该检测过程易于扩展，更快，更准确，从而为SHM数据预处理的强大潜力提供了一种新的视角。

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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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 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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