Contribution to the study and the early detection of bearing faults in synchronous machines

Transport electrification is a major theme in the energy transition. It involves the use of electric technologies in various modes of transportation, such as cars, buses, bicycles, and others. Permanent magnet synchronous machines are often used in this type of application due to their compact design and high efficiency. This type of machine will therefore be widely used in the field of electric mobility in the coming years, raising the question of its robustness and reliability in highly demanding operating cycles. Electric machines are subject to operating stresses that lead to component degradation. The resulting failures can be found in the bearings, stator, rotor, or other components. Bearings are one of the most susceptible components due to the high mechanical, thermal and electrical stresses that lead to gradual degradation through fatigue and can result in the appearance of point defects. The advent of power electronics systems based on wide band gap semiconductors such as SiC or GaN further exacerbates this problem. Actual work on this type of fault avoids destruction of the machine but does not allow optimal management of maintenance schedules. The difficulty with this type of faut lies in the access to relevant information for detection and in the sensitivity of the signals used to achieve it. In this context, the aim of the proposed thesis is to develop an approach enabling early detection of bearing faults, sufficiently in advance that they have not yet led to failures blocking its operation. The objective is to develop a reliable identification method that can form part of a predictive maintenance strategy designed to avoid false alarms

IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see our WEB site. IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions

Academic requirements Master 2 or engineering degree, specializing in electrical engineering

Language requirements English level B2 (CEFR)

Other requirements Strong knowledge of electromagnetics, electrical machine modeling and automation/signal processing