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.

Three-phase induction motors are widely applied in industrial systems due to their durability and efficiency. However, electrical faults such as inter-turn short circuits can compromise performance, leading to unplanned downtime and maintenance costs. Traditional fault detection methods rely on stator current or vibration analysis, each with limitations regarding sensitivity to specific failure modes and dependence on motor power ratings. Despite advancements in non-invasive sensing, challenges remain in balancing fault detection accuracy, computational efficiency, and adaptability to real-world conditions. This study proposes a stray flux-based method for detecting inter-turn short circuits using an externally mounted search coil sensor, eliminating the need for intrusive modifications and enabling fault detection independent of motor power. To account for variations in fault manifestation, the method was evaluated with three different relative positions between the search coil and the faulty winding. Feature extraction and selection are performed using a hybrid strategy combining random forest-based ranking and collinearity filtering, optimizing classification accuracy while reducing computational complexity. Two classification tasks were conducted: binary classification to differentiate between healthy and faulty motors, and multiclass classification to assess fault severity. The method achieved 100% accuracy in binary classification and 99.3% in multiclass classification using the full feature set. Feature reduction to eight attributes resulted in 92.4% and 85.4% accuracy, respectively, demonstrating a trade-off between performance and computational efficiency. The results support the feasibility of deploying stray flux-based fault detection in industrial applications, ensuring a balance between classification reliability, real-time processing, and potential implementation in embedded systems with limited computational resources.