Efficient data-driven defect detection techniques are crucial for maintaining service quality and providing early warnings for infrastructure systems. To this end, we proposed an effective unsupervised anomaly detection framework (DEGAN) using Generative Adversarial Networks (GANs). The framework relies solely on normal time series data as input to train well-configured discriminators into standalone anomaly predictors by leveraging repeatedly collected data from an infrastructure system. Expected normal patterns in data are identified by generators, and well-configured discriminators are extracted to evaluate anomalies in unseen time series. Kernel density estimation (KDE) is coupled with discriminators for probabilistic anomaly detection. Through a Class I railroad track case study, we evaluated the performance of a convolutional DEGAN in detecting anomalies identified by operators, achieving recall and precision of 80% and 86%, respectively. We also investigated the influence of GAN architectures and parameters, model validation scheme (supervised vs. unsupervised), clustering, and the KDE parameters.

中文翻译：

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使用生成对抗网络鉴别器和基础设施系统密度估计进行时间序列异常检测


高效的数据驱动的缺陷检测技术对于维持服务质量和为基础设施系统提供早期预警至关重要。为此，我们提出了一种使用生成对抗网络（GAN）的有效无监督异常检测框架（DEGAN）。该框架仅依赖于正常时间序列数据作为输入，通过利用从基础设施系统重复收集的数据，将配置良好的鉴别器训练为独立的异常预测器。生成器识别数据中预期的正常模式，并提取配置良好的鉴别器来评估未见过的时间序列中的异常情况。核密度估计（KDE）与用于概率异常检测的判别器相结合。通过 I 类铁路轨道案例研究，我们评估了卷积 DEGAN 在检测操作员识别的异常方面的性能，分别实现了 80% 和 86% 的召回率和精度。我们还研究了 GAN 架构和参数、模型验证方案（监督与无监督）、聚类和 KDE 参数的影响。