Study of bearing faults in synchronous machines

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à gagner en efficacité énergétique ;
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à améliorer l’empreinte environnementale de l’industrie et des transports ;

tout en répondant à la demande mondiale en mobilité, en énergie et en produits pour la chimie.

Dans cet objectif, IFPEN développe des solutions permettant, d’une part, d’utiliser des sources d’énergie alternatives et, d’autre part, d’améliorer les technologies existantes liées à l’exploitation des énergies fossiles.

Study of bearing faults in synchronous machines

Today, electric machines are found in the vast majority of soft mobility vehicles (scooters, bicycles) and land vehicles (electric and hybrid). Bearings are among the most fragile components of these rotating machines due to the high mechanical, thermal and electrical stresses that gradually lead to fatigue degradation and occasional failures. The advent of power electronics systems based on WBG components (such as SiC or GaN) further exacerbates this problem. According to the scientific literature, bearing failures are the main cause of rotating machine failures and motor failures in electric vehicles.

Currently, existing work on this type of fault can prevent the destruction of the machine but cannot optimize the management of maintenance schedules. A difficulty in detecting this type of fault lies in the access to information and the sensitivity of the signals used for detection.

In this context, the proposed internship will focus on methods to detect bearing faults before they reach an advanced state leading to failure. The goal is to develop a reliable identification method that can be part of a predictive maintenance strategy to avoid false alarms. In other words, the goal is to anticipate the occurrence of a machine failure before it has consequences that could disrupt its operation.

The internship consists of three main phases:

A bibliographical study of bearing failures, their modeling and detection methods,
Simulation development of a representative model to study this type of failure,
Development and evaluation of a detection method for this type of fault.

Profile required:

Engineering schools or equivalent in electrical engineering with strong knowledge of electromagnetics, electrical machine modeling and automation/signal processing.