Zeina AL MASRY

FEMTO-ST institute and École nationale supérieure de mécanique et des microtechniques (ENSMM)

Lecture Information:
  • April 8, 2022
  • 2:00 PM
  • https://fiu.zoom.us/j/98295344358?pwd=cDBBZlpKenFhcE82bEUrYjFZalBLdz09

Speaker Bio

Dr. Zeina AL MASRY is an associate professor at FEMTO-ST Institute and ENSMM in Besançon in France since 2017. She received her Ph.D. thesis in Applied Mathematics in 2016 from the Université de Pau et des Pays de l’Adour in France. Her research interests concern data quality, applied statistics and stochastic processes. She is focusing on interdisciplinary research to link theory and applications and mainly in diagnosis and prognosis. She is leading a project on developing a connected bra for breast cancer detection. She has several collaborations in industry with big and small groups and in healthcare with hospitals in the east of France.

Description

Safety and dependability are a crucial issue in many industries, which has led to the development of a huge literature devoted to the so-called reliability theory. Roughly speaking, any mechanical or electrical equipment deteriorates over time until failure occurs. For example, power electronic converters such as IGBT semiconductor devices are one of the most critical parts in terms of failure rate and lifetime. Another example is the new generation of instruments in the field of medical robotics that must be reliable in order to ensure optimal performance and safety during the surgical procedure. Hence, the need to optimize the life-cycle performance of deteriorating systems is imperative. Stochastic models are used to describe the random evolution of the equipment over time. When modeling accumulative (non-decreasing) deterioration, standard gamma processes are commonly considered. However, a gamma process is not always a suitable choice in an applicative context. The aim of the presentation is twofold. Firstly, gamma processes for degradation modeling are introduced and a technical methodology for reliability and lifetime prediction is presented. Secondly, the benefits of the extension of such processes are discussed.