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首页> 外文期刊>Structural health monitoring >Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage
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Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage

机译：使用一流的支持向量机使用深度自动编码器的无监督深度学习方法来检测损坏

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This article proposes an unsupervised deep learning–based approach to detect structural damage. Supervised deep learning methods have been proposed in recent years, but they require data from an intact structure and various damage scenarios of monitored structures for their training processes. However, the labeling work on the training data is typically time-consuming and costly, and sometimes collecting sufficient training data from various damage scenarios of infrastructures in service is impractical. In this article, the proposed unsupervised deep learning method based on a deep auto-encoder with an one-class support vector machine only uses the measured acceleration response data acquired from intact or baseline structures as training data, which enables future structural damage to be detected. The major contributions and novelties of the proposed method are as follows. First, an appropriate deep auto-encoder is carefully designed through comparative studies on the depth of neural networks. Second, the designed deep auto-encoder is taken as an extractor to obtain damage-sensitive features from the measured acceleration response data, and an one-class support vector machine is used as a damage detector. Third, experimental and numerical studies validate the high accuracy of the proposed method for damage detection: a 97.4% mean average for a 12-story numerical building model and a 91.0% accuracy for a laboratory-scaled steel bridge. Fourth, the proposed method also detects light damage (i.e. a 10% reduction in stiffness) with 96.9% to 99.0% accuracy, which shows its superior performance compared with the current state of the art. Fifth, it provides stable and more robust damage detection performance with reduced tuning parameters.
机译：本文提出了一种无监督的基于深度学习的方法来检测结构损伤。近年来提出了监督的深度学习方法，但它们需要从完整的结构和监控结构的各种损坏方案的数据进行培训流程。然而，培训数据的标签工作通常是耗时且昂贵的，有时从服务中的基础架构的各种损坏方案中收集足够的训练数据是不切实际的。在本文中，基于具有单级支持向量机的深度自动编码器的提议无监督的深度学习方法仅使用从完整或基线结构获取的测量的加速度响应数据作为训练数据，这使得能够检测到未来的结构损坏。拟议方法的主要贡献和诺克特如下。首先，通过对神经网络深度的比较研究精心设计适当的深度自动编码器。其次，设计的深度自动编码器作为提取器，以获得来自测量的加速度响应数据的损坏敏感功能，并且单级支持向量机用作损坏检测器。三，实验和数值研究验证了损伤检测方法的高精度：12层数值建筑模型的平均值为97.4％，对实验室钢桥的精度为91.0％。第四，该方法还检测光损伤（即刚度10％的10％），精度为96.9％至99.0％，表明其与现有技术的卓越性能。第五，它提供稳定，更强大的损坏检测性能，调谐参数减少。

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