As an alternative to the problems found in the FT-based methods, recent works propose the use of other advanced signal processing methods. [PERSON] and [PERSON] [12] use the wavelet transform (WT) for differentiating internal faults from magnetizing intrush currents in the presence of current transformer saturation. In [13], the empirical WT is used by [PERSON] and [PERSON] to detect winding deformations. [PERSON] et al. [14] combine the use of an improved empirical WT and extreme machine learning technique to diagnose various fault transformer conditions. DC bias in power transformers is detected by using the wavelet packet energy distribution [15]. Other WT variations, i.e., the probabilistic WT, are presented by [PERSON] et al. [16] for the partial discharge measurement of a transformer. On the other hand, in recent years, the use of decomposition methods has also been explored [17; 18; 19; 20; 21]. A comparison between the empirical mode decomposition (EMD) method, the ensemble EMD (EEMD) method, and the complete EEMD (CEEMD) to diagnose short circuit faults is presented by [PERSON] et al. [18]. An extension of the CEEMD, namely, CEEMD with adaptive noise (CEEMDAN), is presented in [19] to detect winding and core loosening. In addition, the variational mode decomposition (VMD) is used by [PERSON] et al. [20] to assess the degradation of transformer windings. Finally, the nonlinear mode decomposition method is present by [PERSON] et al. [21] to diagnose SCTs. Although promising results have been reported with these techniques, some issues still need to be taken into account. For instance, it can be highlighted that the WT is a method designated to decompose nonstationary signals (i.e., signals with changing characteristics) into a set of frequency bands; however, its good results depend mainly on the proper choice of both the decomposition level and the mother wavelet [22]. On the other hand, the HHT, which combines the empirical mode decomposition with the Hilbert transform, is an adaptive method capable of estimating the instantaneous frequencies contained in a signal, but it suffers from a phenomenon called mode mixing (i.e., waves with different frequency are assigned to the same mode or frequency band) [23], which can limit the correct monitoring of the transformer condition. From the methods reviewed above, it is evident that each of them can have certain advantages or disadvantages in specific scenarios. In this regard, it is of great importance to select, combine, modify, and consequently leverage their advantages in order to develop a more efficient method in terms of processing time and complexity, as well as applicability. For instance, FT-based methods have significant advantages such as a low computation burden and do not require meticulous calibration [24]; on the other hand, STFT or WT-based methods can handle nonstationary signals, as in the case of vibration signals in transformers, particularly when the vibrations generated during the starting transient are considered [21]. Similarly, the capabilities of the WT can be utilized to reduce the inherent noise in the vibration signals. This task is particularly challenging when the noise needs to be removed from both transient and steady-state signals without compromising the frequency information necessary for the diagnosis. Therefore, in the search of a more efficient and robust method for the detection of short-circuited turns (SCTs) in transformers, it is essential that the employed method can promptly identify the damage in both transient and steady state operating regimes, thus expanding its applicability across of a wider range of transformer applications. With this goal in mind, this paper contributes the adroit integration of a new method that combines different signal processing techniques to effectively detect and quantify the severity of SCTs in transformers in both regimes of operation, providing a more comprehensive and accurate diagnosis method.