PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Lead Author: Sergio Cofre-Martel Co-author(s): Enrique Lopez Droguett eald@ucla.edu Mohammad Modarres modarres@umd.edu

Physics-Informed Neural Networks for Remaining Useful Life Estimation for Mechanical Systems
Prognostics and health management (PHM) has become a key instrument in the reliability community. Great efforts have gone into estimating systems’ remaining useful life (RUL) by taking advantage of monitoring data and data-driven models (DDMs). The latter have gained significant attention since they are model-independent and do not require previous knowledge of the system under study, known as black-box behavior. Nevertheless, DDMs developed for PHM frameworks are commonly tested on simulated or experimental datasets, which do not present the characteristics and intricacies of data collected from monitoring sensor networks in real systems. Furthermore, the black-box behavior hinders DDMs’ interpretability, and thus they are difficult to trust in the maintenance decision-making process. In this regard, physics-informed models have been implemented through hybrid models, which present significant improvements in accuracy and interpretability. Particularly, physics-informed neural networks (PINNS) have been proposed in deep learning (DL) to either solve or discover partial differential equations that govern a system. This paper presents an implementation of a PINN-RUL model to a case study from a real system. The system consists of a vapor recovery unit (VRU) at an off-shore oil production platform. Challenges when creating RUL labels based on maintenance logs are discussed. Results show that the PINN-RUL architecture is competitive with other traditional approaches, and it allows the interpretation of the system’s degradation dynamics through a latent variable.

Physics-Informed Neural Networks for Remaining Useful Life Estimation for Mechanical Systems

Prognostics and health management (PHM) has become a key instrument in the reliability community. Great efforts have gone into estimating systems’ remaining useful life (RUL) by taking advantage of monitoring data and data-driven models (DDMs). The latter have gained significant attention since they are model-independent and do not require previous knowledge of the system under study, known as black-box behavior. Nevertheless, DDMs developed for PHM frameworks are commonly tested on simulated or experimental datasets, which do not present the characteristics and intricacies of data collected from monitoring sensor networks in real systems. Furthermore, the black-box behavior hinders DDMs’ interpretability, and thus they are difficult to trust in the maintenance decision-making process. In this regard, physics-informed models have been implemented through hybrid models, which present significant improvements in accuracy and interpretability. Particularly, physics-informed neural networks (PINNS) have been proposed in deep learning (DL) to either solve or discover partial differential equations that govern a system. This paper presents an implementation of a PINN-RUL model to a case study from a real system. The system consists of a vapor recovery unit (VRU) at an off-shore oil production platform. Challenges when creating RUL labels based on maintenance logs are discussed. Results show that the PINN-RUL architecture is competitive with other traditional approaches, and it allows the interpretation of the system’s degradation dynamics through a latent variable.

Paper SC17 Preview

Author and Presentation Info

Lead Author Name: Sergio Cofre Martel (scofre@umd.edu)

Bio:

Country: United States of America
Company:
Job Title:

A PSAM Profile is not yet available for the presenter.

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Paper SC17 Preview

Author and Presentation Info

Download paper SC17.

Download the presentation PowerPoint file.