In this work, we present a new approach for latent system
dynamics and remaining useful life (RUL) estimation of
complex degrading systems using generative modeling and
reinforcement learning. The main contributions of the proposed
method are two-fold. First, we show how a deep generative
model can approximate the functionality of high-fidelity
simulators and, thus, is able to substitute expensive and
complex physics-based models with data-driven surrogate
ones. In other words, we can use the generative model in lieu of
the actual system as a surrogate model of the system.
Furthermore, we show how to use such surrogate models for
predictive analytics. Our method follows two main steps. First,
we use a deep variational autoencoder (VAE) to learn the
distribution over the latent state-space that characterizes the
dynamics of the system under monitoring. After model training,
the probabilistic VAE decoder becomes the surrogate system
model. Then, we develop a scalable reinforcement learning
framework using the decoder as the environment, to train an
agent for identifying adequate approximate values of the latent
dynamics, as well as the RUL.
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A Generative Reinforcement Learning Framework for Predictive Analytics
In this work, we present a new approach for latent system dynamics and remaining useful life (RUL) estimation of complex degrading systems using generative modeling and reinforcement learning. The main contributions of the proposed method are two-fold. First, we show how a deep generative model can approximate the functionality of high-fidelity simulators and, thus, is able to substitute expensive and complex physics-based models with data-driven surrogate ones. In other words, we can use the generative model in lieu of the actual system as a surrogate model of the system. Furthermore, we show how to use such surrogate models for predictive analytics. Our method follows two main steps. First, we use a deep variational autoencoder (VAE) to learn the distribution over the latent state-space that characterizes the dynamics of the system under monitoring. After model training, the probabilistic VAE decoder becomes the surrogate system model. Then, we develop a scalable reinforcement learning framework using the decoder as the environment, to train an agent for identifying adequate approximate values of the latent dynamics, as well as the RUL.To our knowledge, the method presented in this paper is the first in industrial prognostics that utilizes generative models and reinforcement learning in that capacity. While the process requires extensive data preprocessing and environment tailored design, which is not always possible, it demonstrates the ability of generative models working in conjunction with reinforcement learning to provide proper value estimations for system dynamics and their RUL. To validate the quality of the proposed method, we conducted numerical experiments using the train_FD002 dataset provided by the NASA CMAPSS data repository. Different subsets were used to train the VAE and the RL agent, and a leftover set was then used for model validation. The results shown prove the merit of our method and will further assist us in developing a data-driven RL environment that incorporates more complex latent dynamic layers, such as normal/faulty operating conditions and hazard processes.
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- Award ID(s):
- 1846975
- NSF-PAR ID:
- 10462985
- Date Published:
- Journal Name:
- Proceedings annual Reliability and Maintainability Symposium
- ISSN:
- 2577-0993
- Page Range / eLocation ID:
- 1-7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
