SHMnet: Condition assessment of bolted connection with beyond human-level performance
SHMnet: Condition assessment of bolted connection with beyond human-level performance
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DOI：
10.1177/1475921719881237
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Tong Zhang;S. Biswal;Ying Wang
中科院分区：
文献类型：
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作者：
Tong Zhang;S. Biswal;Ying Wang
深度学习算法正在以传统方法无法竞争的准确度改变各种研究领域。近年来，越来越多的研究工作已投入到结构健康监测领域。在这项工作中，我们提出了一种新的深度卷积神经网络，即SHMnet，用于具有挑战性的结构状态识别案例，即螺栓连接损坏的钢框架。我们对网络架构的优化和训练数据的准备进行了系统的研究。在实验室中，对钢框架进行了多次冲击锤试验，不同的螺栓连接损坏情况下，小到一个螺栓松动。来自单个加速度计的时域监测数据用于训练。我们对不同的层数，不同的传感器位置，训练数据集的数量和噪声水平进行了参数研究。结果表明，建议的SHMnet是有效的和可靠的，至少有四个独立的训练数据集，并避免振动节点作为传感器的位置。在高达60%的加性高斯噪声下，平均识别准确率超过98%。相比之下，传统的基于模态参数识别的方法不可避免地失败，由于识别的固有频率和振型的变化不明显。结果提供了信心，使用开发的方法作为一个有效的结构状态识别框架。它具有改变结构健康监测实践的潜力。代码和相关信息可以在https://github.com/capepoint/SHMnet上找到。
Deep learning algorithms are transforming a variety of research areas with accuracy levels that the traditional methods cannot compete with. Recently, increasingly more research efforts have been put into the structural health monitoring domain. In this work, we propose a new deep convolutional neural network, namely SHMnet, for a challenging structural condition identification case, that is, steel frame with bolted connection damage. We perform systematic studies on the optimisation of network architecture and the preparation of the training data. In the laboratory, repeated impact hammer tests are conducted on a steel frame with different bolted connection damage scenarios, as small as one bolt loosened. The time-domain monitoring data from a single accelerometer are used for training. We conduct parametric studies on different layer numbers, different sensor locations, the quantity of the training datasets and noise levels. The results show that the proposed SHMnet is effective and reliable with at least four independent training datasets and by avoiding vibration node points as sensor locations. Under up to 60% additive Gaussian noise, the average identification accuracy is over 98%. In comparison, the traditional methods based on the identified modal parameters inevitably fail due to the unnoticeable changes of identified natural frequencies and mode shapes. The results provide confidence in using the developed method as an effective structural condition identification framework. It has the potential to transform the structural health monitoring practice. The code and relevant information can be found at https://github.com/capepoint/SHMnet.