Multi-modal generative adversarial networks for synthesizing time-series structural impact responses

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.