GAGNN: Generative Adversarial Network and Graph Neural Network for Prognostic and Health Management

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Abstract
Thanks to the development of the Internet of Things, a large number of sensors have been deployed, resulting in the collection of abundant time series data. These data series contain potential space-time connection and noise at the same time. Prediction and health management (PHM) aim to provide decision support based on the health state of an entire engineering system. Additionally, the predicting future values also contribute to decision making and fall under the category of time series forecasting. While the existing methods focus on capturing the correlation between the data and reducing the impact of noise, they often fail to fully utilize the noise present in the time series data. In this article, we propose a framework for multivariate time series forecasting called GAGNN. This framework integrates the idea of a generative adversarial network and a graph neural network organically. It adopts the graph neural network as the generator and a multilayer perceptron as the discriminator. Finally, the prediction module is used to obtain the prediction results. The generator, discriminator, and prediction module are trained jointly. Our experimental results demonstrate that our model outperforms the original model on most benchmark data sets and achieves the best results on three out of six benchmark data sets.
Funding Information
  • Key Technology Research and Development Program of the Zhejiang Province (2022C01125, 2024C01212)
  • Zhejiang Province High-Level Talent Special Support Program-Leading Talent of Technological Innovation (2022R52043)

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