Abstract:
Maintenance is becoming increasingly important in companies and tends to evolve for
reactivity and cost needs. A particular evolution concerns the way to apprehend the phenomena
of failure: little by little the industrialists tend, not only to anticipate them by the recourse to
preventive actions, but in addition to do it in the most just possible way with a goal reducing
costs and risks. This evolution has given a growing share to the prognosis process.
The activity of fault prognosis is today considered as a key process in industrial
maintenance strategies. However, in practice, prognostic tools are still rare. Today's stabilized
approaches rely on a history of significant incidents to be representative of potentially
predictable events
The purpose of this thesis is to propose a tool to predict the degradation of equipment
without prior knowledge of its behavior, and to generate prognostic indicators to optimize
maintenance strategies. Various techniques, of vibratory signal processing, have been explored
and tested, on a test bench designed and realized as part of the research axes of this work. Two
techniques of artificial intelligence have been exploited in the diagnosis and prognosis of defects
in rotating machines, where indicator selection techniques have been explored.
The combination of vibration signal processing techniques and artificial intelligence by neural
networks has made it possible to provide an efficient prognostic tool and to quantify the
relevance of the sources of information used and proposed.
