Modeling Degradation Systems with Latent States: A Framework with State Space Models and Recurrent Neural Network
To monitor the dynamic behavior of degrading systems over time, a generic hierarchical discrete-time state-space model (SSM) is introduced that can formulate the stochastic evolution of the latent states (discrete, continuous, or hybrid) of a degrading system and dynamic sensors while system is operating. This generic SSM is inspired by Bayesian hierarchical modeling and recurrent neural networks without imposing strong parametric and distributional assumptions and prior of the system dynamics. The proposed framework is supported by experimental results using multiple datasets including NASA C-MAPSS dataset.
How to Cite
degradation model, sensor-driven systems, state-space model, Bayesian network, Recurrent neural network, Reliability, System health monitoring
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.