Multi-modal generative adversarial networks for synthesizing time-series structural impact responses
Multi-modal generative adversarial networks for synthesizing time-series structural impact responses
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DOI：
10.1016/j.ymssp.2023.110725
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发表时间：
2023-12
影响因子：
8.4
通讯作者：
Zhymir Thompson;Austin Downey;Jason D. Bakos;Jie Wei;Jacob Dodson
中科院分区：
文献类型：
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作者：
Zhymir Thompson;Austin Downey;Jason D. Bakos;Jie Wei;Jacob Dodson
验证新定义的状态观测器的过程可能需要从仪器中收集大量数据。然而，收集高速率动态事件（短时间尺度的影响和冲击）的数据可能非常昂贵。此外，为收集数据而进行的实验可以提供高度可变的结果，而高能冲击将损伤引入到被测试的结构中，从而导致后续测试的不同结果。本文提出使用生成对抗网络（GAN）来生成数据，以补充验证状态观测器所需的实验数据集。GAN是一类深度学习模型，用于生成与训练数据在统计上相当的数据。由于它们在推理过程中的一致性、速度和可移植性，它们是验证的理想候选者。在这项工作中，从冲击下的电子封装中收集的数据被用来检查GAN的生成能力。本文提出了一种有条件的Wasserstein GAN（CWGAN）实现的合成高速率的动态振动的生产，并介绍了有条件的输入到评论家在最后一层，而不是第一层。结果表明，所提出的生成模型能够产生与所提供的训练数据在统计上相似的数据。将生成的数据与训练数据进行比较，并探讨模型的优点和局限性。模型及其工件作为本文的补充材料提供，并通过公共存储库共享。
The process of validating newly-defined state observers can potentially require a significant amount of data gathered from instrumentation. However, collecting data for high-rate dynamic events (short time-scale impact and shock) can be very expensive. Additionally, experiments performed for collecting data can provide highly variable results while the high-energy impacts introduce damage into the structure being tested, consequently resulting in different results for subsequent tests. This paper proposes the use of Generative Adversarial Networks (GANs) to generate data that supplements the experimental datasets required for the validation of state observers. GANs are a class of deep learning models used for generating data statistically comparable to that on which it was trained. They are an ideal candidate for validation due to their consistency, speed, and portability during inference. In this work, data collected from an electronics package under shock is used to examine the generative ability of GANs. This paper proposes a conditional Wasserstein GAN (CWGAN) implementation for the production of synthetic high-rate dynamic vibrations, and introduces the conditional input to the critic at the layer towards the end as opposed to the first layers. Results suggest the generative model proposed is capable of producing data statistically similar to the provided training data. The generated data is compared to the training data, and the advantages and limitations of the model are explored. The model and its artifacts are provided as supplemental material to this article and shared through a public repository.