This study proposes an unsupervised, online structural health monitoring framework robust to the sensor configuration, that is, the number and placement of sensors. The proposed methodology leverages generative adversarial networks (GANs). The GAN's discriminator network is the novelty detector, while its generator provides additional data to tune the detection threshold. GAN models are trained with the fast Fourier transform of structural accelerations as input, avoiding the need for any structure-specific feature extraction. Dense, convolutional (convolutional neural network), and long short-term memory (LSTM) units are evaluated as discriminators under different GAN training loss patterns, that is, the differences between discriminator and generator training losses. Results show that the LSTM-based discriminators and the suggested threshold tuning technique to be robust even in loss patterns with overfitted discriminators, a probable outcome of limited training sets. The framework is evaluated on two benchmark datasets. With only 100 s of training data, it achieved 95% novelty detection accuracy, distinguishing between different damage classes and identifying their resurgence under varying sensor configurations. Finally, the majority-vote-ensemble of discriminator-generator pairs at different training epochs is introduced to reduce false alarms, improve novelty detection accuracy and stability.

中文翻译：

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在结构健康监测中建立一个通用的无监督新奇检测框架

本研究提出了一种对传感器配置（即传感器的数量和位置）具有鲁棒性的无监督在线结构健康监测框架。所提出的方法利用了生成对抗网络（GAN）。GAN 的鉴别器网络是新奇检测器，而它的生成器提供额外的数据来调整检测阈值。GAN 模型使用结构加速度的快速傅里叶变换作为输入进行训练，避免了任何特定于结构的特征提取的需要。密集、卷积（卷积神经网络）和长短期记忆（LSTM）单元在不同的 GAN 训练损失模式下被评估为判别器，即判别器和生成器训练损失之间的差异。结果表明，基于 LSTM 的鉴别器和建议的阈值调整技术即使在具有过拟合鉴别器的损失模式中也很稳健，这是有限训练集的可能结果。该框架在两个基准数据集上进行评估。仅使用 100 秒的训练数据，它就实现了 95% 的新奇检测准确率，区分不同的损坏类别并识别它们在不同传感器配置下的复苏。最后，引入了不同训练时期的判别器-生成器对的多数投票集成，以减少误报，提高新奇检测的准确性和稳定性。它实现了 95% 的新奇检测准确率，区分不同的损坏类别并识别它们在不同传感器配置下的复苏。最后，引入了不同训练时期的判别器-生成器对的多数投票集成，以减少误报，提高新奇检测的准确性和稳定性。它实现了 95% 的新奇检测准确率，区分不同的损坏类别并识别它们在不同传感器配置下的复苏。最后，引入了不同训练时期的判别器-生成器对的多数投票集成，以减少误报，提高新奇检测的准确性和稳定性。