Structural health monitoring of mechanical systems is essential to avoid their catastrophic failure. In this article, an effective deep neural network is developed for extracting the damage-sensitive features from frequency data of vibration signals to damage detection of mechanical systems in the presence of the uncertainties such as modeling errors, measurement errors, and environmental noises. For this purpose, the finite element method is used to analyze a mechanical system (finite element model). Then, vibration experiments are carried out on the laboratory-scale model. Vibration signals of real intact system are used to updating the finite element model and minimizing the disparities between the natural frequencies of the finite element model and real system. Some parts of the signals that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition technique. Frequency domain decomposition method is used to extract frequency data. The proposed deep neural network is trained using frequency data of the finite element model and real intact state and then is tested using frequency data of the real system. The proposed network is designed in two stages, namely, the pre-training classification based on deep auto-encoder and Softmax layer (first stage), and the re-training classification based on backpropagation algorithm for fine tuning of the network (second stage). The proposed method is validated using a lab-scale offshore jacket structure. The results show that the proposed method can learn features from the frequency data and achieve higher accuracy than other comparative methods.

中文翻译：

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使用有限元模型和真实完整状态的振动信号进行基于深度神经网络的损伤检测：通过实验室规模的海上导管架结构进行评估

机械系统的结构健康监测对于避免其灾难性故障至关重要。在本文中，开发了一种有效的深度神经网络，用于在存在建模误差、测量误差和环境噪声等不确定性的情况下，从振动信号的频率数据中提取损伤敏感特征，以进行机械系统的损伤检测。为此，使用有限元方法来分析机械系统（有限元模型）。然后，在实验室规模的模型上进行振动实验。真实完整系统的振动信号用于更新有限元模型并最小化有限元模型与真实系统的固有频率之间的差异。使用完整的集成经验模态分解技术去除与系统性质无关的某些信号部分。频域分解方法用于提取频率数据。所提出的深度神经网络使用有限元模型的频率数据和真实的完整状态进行训练，然后使用真实系统的频率数据进行测试。所提出的网络设计为两个阶段，即基于深度自动编码器和 Softmax 层的预训练分类（第一阶段），以及基于反向传播算法对网络进行微调的重新训练分类（第二阶段） . 使用实验室规模的海上导管架结构验证了所提出的方法。