Assoc. Prof. Khanh T. P. Nguyen ⭐

Senior Lecturer at Tarbes National School of Engineering (ENIT), Toulouse INP, France.


Assoc. Prof. Khanh T. P. Nguyen
Senior lecturer at Tarbes National School of Engineering (ENIT), Toulouse INP, France.

Khanh T. P. Nguyen is an Associate Professor at Tarbes National School of Engineering (ENIT), Toulouse INP, France, and a permanent member of its Production Engineering Laboratory (LGP) since 2017. She obtained her Ph.D. degree in Automation and Production Engineering from Ecole Centrale de Nantes, France in 2012. Her current research interests include applications of Artificial Intelligence in Prognostics & Health Management (PHM) and Predictive Maintenance. She has been the principal investigator of a regional project, scientific leader for the ENIT parts of an EU-funded project and a joint research project with ALSTOM. Previously, she was also involved in a French national project and a European project in the area of dependability assessment of intelligent transportation systems.

Khanh has co-organized special sessions and invited tracks at highly recognized international conferences in the fields of reliability and PHM such as ESREL, IFAC World Congress, and European Conferences of the PHM Society. She was a member of the Program Committees of the fifth & sixth European Conferences of PHM Society (2020 & 2021), ESREL 2022, and a member of the International Committee of the 2021 PHM Asia Pacific. She is also co-chairs of Doctoral Symposium of PHME 2022.
On research collaboration, besides her talented colleagues at LGP, Khanh has widely collaborated with more than 10 external researchers at UTT Troyes, INPG Grenoble, CRAN Lorraine, UPC Spain, UNIBO Italy, USAC Chile, and Polytech Montreal Canada. Those collaborations have led to 20+ articles published in top-quality journals and 20+ papers presented at and published by prestigious international conferences. More information about her publications can be found at the link.


Title: Dealing with uncertainty in prognostics and health management of multi-component systems 

Abstract: In context of Industry 4.0, prognostics and health management (PHM) of complex industrial systems is increasingly a key challenge for guaranteeing system reliability and reducing lifetime operational cost. Accurate predictions of remaining useful life time (RUL) of equipment provide valuable information for maintenance organization, thus avoiding systems breakdowns and improving their overall performance. However, as prognostics deals with prediction of future system behavior, several sources of uncertainties exist in RUL predictions.
For dealing with uncertainty in prognostics, numerous studies in literature use stochastic models to characterize the degradation process and predict the RUL distribution. However, in practice, it is difficult to derive stochastic models to capture degradation mechanisms of complex physical systems. Besides, the outstanding achievements in sensing technologies have facilitated the development of data-driven methods. Among them, deep learning methods become one of the most popular trends in recent studies; but they usually provide point predictions without quantifying the output uncertainties. To address this litterature gap, we present a new probabilistic deep leaning methodology for uncertainty quantification of multi-component systems’ RUL. It is a combination of a probabilistic model and a deep recurrent neural network to predict the components’ RUL distributions. Then, using the information about the system’s architecture, the formulas to quantify system reliability or system-level-RUL uncertainty are derived. The performance of the proposed methodology is investigated through the benchmark data provided by NASA. The obtained results highlight the point prediction accuracy and the uncertainty management capacity of the proposed methodology. In addition, thanks to the explicit RUL distributions of components, the system reliability for different structures is obtained with high accuracy, especially for series structures.

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