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Mayank Shekhar Jha
Researcher at University of Lorraine
Publications - 24
Citations - 191
Mayank Shekhar Jha is an academic researcher from University of Lorraine. The author has contributed to research in topics: Prognostics & Computer science. The author has an hindex of 5, co-authored 16 publications receiving 125 citations. Previous affiliations of Mayank Shekhar Jha include Institut national des sciences appliquées de Toulouse & École centrale de Lille.
Papers
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Journal ArticleDOI
Particle filter based hybrid prognostics for health monitoring of uncertain systems in bond graph framework
TL;DR: The paper’s main objective is to address the problem of health monitoring of system parameters in Bond Graph (BG) modeling framework, by exploiting its structural and causal properties, and a simplified variance adaptation scheme is proposed.
Journal ArticleDOI
Particle filter based hybrid prognostics of proton exchange membrane fuel cell in bond graph framework
Mayank Shekhar Jha,Mathieu Bressel,Mathieu Bressel,Belkacem Ould-Bouamama,Geneviève Dauphin-Tanguy +4 more
TL;DR: An efficient multi-energetic model suited for diagnostics and prognostics, developed using some specific properties of Bond Graph (BG) theory is presented, which achieves very high accuracy and precise confidence bounds.
Journal ArticleDOI
Supervised Health Stage Prediction Using Convolutional Neural Networks for Bearing Wear.
Sungho Suh,Sungho Suh,Joel Jang,Joel Jang,Seungjae Won,Seungjae Won,Mayank Shekhar Jha,Yong Oh Lee +7 more
TL;DR: A novel data-driven health division method based on convolutional neural networks using a graphical representation of time series data, called a nested scatter plot, which is able to detect the early symptoms of bearing wear earlier and more efficiently than other threshold-based health indicator methods.
Proceedings ArticleDOI
A Reinforcement Learning Approach to Health Aware Control Strategy
TL;DR: In this article, reinforcement learning based approach is used to learn an optimal control policy in face of component degradation by integrating global system transition data (generated by an analytical model that mimics the real system) and RUL predictions.
Proceedings ArticleDOI
Robust FDI based on LFT BG and relative activity at junction
TL;DR: A new method of generating robust and adaptive thresholds is developed, where uncertain parameters are treated as intervals that vary between the upper and lower bounds, and applied over the hydraulic system to alleviate the missed alarm problem.