This course is intended for 2nd year students of MSID Master.
Objectives
One major aim of reliability theory is to predict the ability of an industrial system to perform its required functions. This ability is measured through different indicators such as the system reliability, availability, mean residual life, ... The evolution of a system over time is not fixed in advance and stochastic models are used to model its randomness. Once fitted from experimental data, these stochastic models are used to quantify the indicators of interest and make some prediction over the future evolution of the system. They are also of interest to propose well-adapted preventive maintenance strategies, to enlarge the system lifetime and prevent unexpected failures.
The objective of this course is to present basic notions of reliability theory, together with stochastic models and methods, useful in this context.
Outline
- Introduction to reliability theory
- Multi-unit system modeling
- Markovian systems
- Renewal theory and regenerative systems
- Gamma wear process
Prerequisites
Continuous-time Markov processes with finite state space (included in UE Poisson and Markov processes of MSID M1)
- Enseignant: Sophie Mercier
- Enseignant: Christian Paroissin