论文标题
使用变异自动编码器和一级支撑向量机对结构损伤的半监督检测
Semi-supervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine
论文作者
论文摘要
近年来，在结构健康监测（SHM）系统中引入了人工神经网络（ANN）。具有数据驱动方法的半监督方法允许对从未破坏的结构条件获得的数据进行ANN培训，以检测结构性损害。在标准方法中，在训练阶段之后，手动定义决策规则以检测异常数据。但是，可以使用机器学习方法自动制作此过程，该方法使用超参数优化技术最大化了性能。本文提出了一种半监督方法，采用数据驱动的方法来检测结构异常。该方法包括：（i）变异自动编码器（VAE）近似未损坏的数据分布，以及（ii）使用从VAE信号重建中提取的损伤敏感特征来区分不同健康状况的一级支持向量机（OC-SVM）。该方法适用于IASC-ASCE结构健康监测任务组在九个损坏方案中测试的比例尺钢结构。
In recent years, Artificial Neural Networks (ANNs) have been introduced in Structural Health Monitoring (SHM) systems. A semi-supervised method with a data-driven approach allows the ANN training on data acquired from an undamaged structural condition to detect structural damages. In standard approaches, after the training stage, a decision rule is manually defined to detect anomalous data. However, this process could be made automatic using machine learning methods, whom performances are maximised using hyperparameter optimization techniques. The paper proposes a semi-supervised method with a data-driven approach to detect structural anomalies. The methodology consists of: (i) a Variational Autoencoder (VAE) to approximate undamaged data distribution and (ii) a One-Class Support Vector Machine (OC-SVM) to discriminate different health conditions using damage sensitive features extracted from VAE's signal reconstruction. The method is applied to a scale steel structure that was tested in nine damage's scenarios by IASC-ASCE Structural Health Monitoring Task Group.
