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 (QTFDs) have demonstrated the potential to reveal time-variant features and information about the energy distribution of nonstationary signals. Therefore, several methodologies have been proposed lately based on these methods to perform electric machine health accompaniment. This article 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. This article aims to provide valuable information for researchers and professionals seeking to optimize the health monitoring of electric machines in the industrial environment.