Fault diagnosis in electric machines and propellers for electrical propulsion aircraft: A review

Abstract

The present work aims to conduct an extensive literature review on the fault diagnosis and classification in electric machines, especially those with permanent magnets, for aeronautical propulsion applications. The main contribution of this research is to assess how intelligent systems focused on fault detection and diagnosis in electric propulsion systems have evolved over the past five years, what are the main types of algorithms used, and how the rapid advancement of machine learning techniques has impacted this research area. Initially, an introduction to the main diagnostic methods is provided, including techniques based on mathematical models, signal analysis, as well as the use of machine learning and deep learning. Subsequently, a detailed study of the main references found in recent years for each type of fault, whether electrical, magnetic, or mechanical, is undertaken. Regarding aeronautical applications, a study of faults in rotating blades and on coupling systems between an electric motor and a set of propellers is conducted. Throughout the text, some of the main datasets found during the research are presented. These datasets include characteristics of healthy operation and fault of windings, bearings, as well as other mechanical components that can be connected to the machine’s shaft, such as gearboxes. Finally, some statistics from this research are presented showing results regarding the annual distribution of publication of all reviewed references, the proportion of faults addressed in all articles, as well as a detailed analysis of the proportion in which each type of algorithm appears in the cited references.