Induction Machine Fault Diagnosis With Quadratic Time–Frequency Distributions: State of the Art
Avyner L. O. Vitor, Alessandro Goedtel, Marcelo Favoretto Castoldi, Wesley Angelino de Souza, Gustavo Henrique Bazan
Abstract
Production processes in industrial facilities are essentially a dynamic activity, but traditional time domain analysis (TDA) and frequency domain analysis (FDA) assume stationary conditions for induction machine fault diagnosis. The time-frequency domain analysis (TFDA) is an alternative to overcome the inadequacy of TDA and FDA techniques in transitory situations. In this context, the quadratic time-frequency distributions (QTFD) have demonstrated the potential to reveal time-variant features and information about the energy distribution of non-stationary signals. Therefore, several methodologies have been proposed lately based on these methods to perform electric machine health accompaniment. This paper presents a comprehensive survey of recent advancements in the application of QTFD for fault diagnosis in industrial time-varying systems. The concepts of the conventional joint time-frequency decomposition methods and the theoretical framework of the quadratic tools are briefly presented. This compilation promotes insights into the current state-of-the-art of TFDA-based condition monitoring techniques and highlights the progress, limitations, and future prospects in this field. The paper aims to provide valuable information for researchers and professionals seeking to optimize the health monitoring of electric machines in the industrial environment.