Abstract Fundamentally, Structural Health Monitoring (SHM) of mechanical systems is essential to avoid their catastrophic failure. The first key contribution of this paper is presenting a new method for damage detection of mechanical systems in presence of the uncertainties such as modeling errors, measurement errors, varying loading conditions and environmental noises based on Finite Element (FE) model and real healthy state. On the other hand, deep learning has been widely used in image and signal analyses with great success. According to this enhancement, the second key contribution of this paper is designing a developed Deep Convolutional Neural Network (DCNN) with training interference and customized architecture to learn the features. In industrial environments, most structures are exposed to varying environmental conditions and it is difficult to collect data containing real damages, and generally, only the data of a real healthy system is available; therefore, it is necessary to have an effective method for damage detection of real systems based on the artificial damages and real healthy data. From this standpoint, the third key contribution of this paper is training process of the proposed DCNN using raw frequency data of the FE model and real healthy state, which is then tested using the raw frequency data of the real system. The proposed DCNN can directly learn the features from raw frequency data of the FE model and real healthy state and discover the damage-sensitive features in order to damage detection of a real system. In this method, only dynamic responses of real healthy system are used to updating the FE model and minimizing the errors. The efficacy of the proposed method is validated using the experimental beam structure. Time data and several manual features from time and frequency data as well as two intelligent methods are used as comparisons. The results show that the proposed method can learn the features from raw frequency data and achieve higher accuracy than other comparative methods.

中文翻译：

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基于有限元模型和真实健康状态的动态响应开发用于梁状结构损伤检测的深度神经网络

摘要 从根本上说，机械系统的结构健康监测（SHM）对于避免其灾难性故障至关重要。本文的第一个关键贡献是基于有限元 (FE) 模型和真实健康状态，提出了一种新方法，用于在存在建模误差、测量误差、负载条件变化和环境噪声等不确定性的情况下对机械系统进行损坏检测。另一方面，深度学习已广泛用于图像和信号分析，并取得了巨大成功。根据这一改进，本文的第二个关键贡献是设计了一个开发的深度卷积神经网络 (DCNN)，具有训练干扰和定制架构来学习特征。在工业环境中，大多数结构暴露在不同的环境条件下，很难收集包含真实损坏的数据，通常只有真正健康系统的数据可用；因此，需要一种基于人为损伤和真实健康数据对真实系统进行损伤检测的有效方法。从这个角度来看，本文的第三个关键贡献是使用有限元模型的原始频率数据和真实健康状态对所提出的 DCNN 进行训练，然后使用真实系统的原始频率数据进行测试。所提出的 DCNN 可以直接从有限元模型的原始频率数据和真实健康状态中学习特征，并发现损伤敏感特征，以便对真实系统进行损伤检测。在这种方法中，仅使用真实健康系统的动态响应来更新有限元模型并最小化误差。使用实验梁结构验证了所提出方法的有效性。时间数据和来自时间和频率数据的几个手动特征以及两种智能方法被用作比较。结果表明，所提出的方法可以从原始频率数据中学习特征，并且比其他比较方法具有更高的精度。