Ensemble of recurrent neural networks with long short-term memory cells for high-rate structural health monitoring
Ensemble of recurrent neural networks with long short-term memory cells for high-rate structural health monitoring
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DOI：
10.1016/j.ymssp.2021.108201
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发表时间：
2021
影响因子：
8.4
通讯作者：
Vahid Barzegar;S. Laflamme;Chao Hu;J. Dodson
中科院分区：
文献类型：
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作者：
Vahid Barzegar;S. Laflamme;Chao Hu;J. Dodson
部署经历高速动态事件的系统，如高超声速飞行器、先进武器和主动爆炸减缓系统，需要在亚毫秒范围内的高速结构健康监测（HRSHM）能力，以确保持续运行和安全。然而，高速率反馈系统的开发是一项复杂的任务，因为这些动态系统具有以下独特的特征：(1)外部负载的大不确定性；(2)高水平的非平稳性和大干扰；(3)系统配置变化带来的未建模动力学。在本文中，我们提出了一种专门为HRSHM应用设计的深度学习算法。它由具有迁移学习能力的长短期记忆（LSTM）细胞构成的循环神经网络（rnn）的集合组成，以应对训练数据的高度有限可用性，因为它是典型的高速率系统。rnn集合的使用使多速率采样能力能够捕获时间序列的多时间特征，从而实现非平稳性的建模。此外，由于rnn使用短序列lstm并并行排列，因此计算时间大大减少到亚毫秒范围。算法的性能在加速跌落塔测试产生的实验高速率动态数据上进行了研究，并与先前的调查结果进行了基准测试，使用了一种纯粹的边缘算法，称为可变输入空间观测器（VIO）及其混合架构（hybrid VIO），其中包含了物理知识，被认为是算法性能的上限。数值结果表明，由5个rnn组成的rnn集合在估计误差指标上明显优于VIO，其性能接近混合VIO，每个预测步长的平均计算时间为25 μ s。然而，rnn的集合在低振幅激励下表现出抖振，这可能是由于rnn在小时间延迟下采样的行为。对RNN权重和隐藏状态的检查证实了该算法可以捕获多时间特征，并且对训练数据集中的噪声进行了调查，结果表明该算法对于高达20 dB的信噪比具有鲁棒性。
The deployment of systems experiencing high-rate dynamic events, such as hypersonic vehicles, advanced weaponry, and active blast mitigation systems, require high-rate structural health monitoring (HRSHM) capabilities in the sub-millisecond realm to ensure continuous operations and safety. However, the development of high-rate feedback systems is a complex task because these dynamic systems are uniquely characterized by (1) large uncertainties in their external loads,(2) high levels of non-stationarity and heavy disturbance, and (3) unmodeled dynamics from changes in system configuration. In this paper, we present a deep learning algorithm specifically engineered for HRSHM applications. It consists of an ensemble of recurrent neural networks (RNNs) constructed with long short-term memory (LSTM) cells with transfer learning capabilities to cope with the highly limited availability of training data as it is typical for high-rate systems. The use of an ensemble of RNNs empowers multi-rate sampling capability to capture multi-temporal features of the time series, thus enabling modeling of non-stationarities. Also, because the RNNs use short-sequence LSTMs and are arranged in parallel, the computation time is substantially reduced to the sub-millisecond range. The performance of the algorithm is investigated on experimental high-rate dynamic data produced from accelerated drop tower tests and benchmarked against results from a prior investigation using a purely on-the-edge algorithm termed variable input space observer (VIO) and its hybrid architecture (hybrid VIO) that incorporated physical knowledge and is considered as an upper bound on the algorithm performance. Numerical results show that the ensemble of RNNs, composed of five RNNs, significantly outperformed the VIO, with performance close to that of the hybrid VIO when it comes to the estimation error metrics, with an average computation time of 25 μ s per prediction step. Yet, the ensemble of RNNs exhibits chattering for low-amplitude excitations, likely attributable to the behavior of RNNs sampling at small time delays. An examination of the RNN weights and hidden states confirms that the algorithm can capture multi-temporal features, and an investigation with respect to noise in the training dataset showed that the algorithm is robust for signal-to-noise ratios up to 20 dB.