_applied sciences_  
_Article_  
**Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals**  
**[PERSON] \({}^{1}\)\({}^{\copyright}\)**, _[PERSON] \({}^{2}\)\({}^{\copyright}\)**, _[PERSON] \({}^{2}\)_, _[PERSON] \({}^{2}\)\({}^{\copyright}\)_, _[PERSON] \({}^{2}\)\({}^{\copyright}\)_ and _[PERSON] \({}^{3,\bullet}\)_ \begin{tabular}{p{34.1 pt}}
1 ENAP-RG, CA-Fuentes Alternas y Calidad de la Energia Electrica, Departamento de Ingenieria Electromecanica, Tecnologico Nacional de Mexico/ITS de Irapuato, Carretera Irapuato-Silao km 12.5, Colonia El Coqral, Inquato 36821, Mexico, [EMAIL_ADDRESS] \\
2 ENAP-RG, CA Siettens Dimitrios y Control, Facultad de Ingenieria, Universidad Autonoma de Quateraterna (UAQ), Campus San Juan del Rio, Rio Maceuzano 249, San Cayetano, San Juan del Rio 76807, Mexico; roberto.huerta@enap-r.gorg (J.R.H.-R.); [PERSON]@enap-r.gorg (J.L.G.-C.); [PERSON]] (J.P.A.-S.); martin.valtierra@enap-r.gorg (M.V.-R.) \\
3 ENAP-RG, Division de Ingenieria, Universidad de Guanajuato (UG), Campus Irapuato-Silamanca, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Mexico \\
* Correspondence: david.camarena@enap-r.gorg \\ \end{tabular}   
**Citation:** [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals. _Appl. Sci._**2023**, _13_, 12218. [[https://doi.org/10.3309/appl32212218](https://doi.org/10.3309/appl32212218)]([https://doi.org/10.3309/appl32212218](https://doi.org/10.3309/appl32212218))  
**Academic Editor:** [PERSON]  
Received: 3 October 2023  
Received: 2 November 2023  
Received: 8 November 2023  
Received: 2 November 2023  
Received: 8 November 2023  
Received: 2 November 2023  
Received: 3 October 2023  
diverse faults that can arise in a transformer, faults in the windings (FW), representing about 50% of all failures in a transformer [4], have received special interest from researchers because these elements are the most important and vulnerable in transformers. Therefore, if this fault is not addressed early, it can increase costs for users and affect industrial processes. In addition, it can damage other elements of the transformer, and depending on the number of elements damaged, a failure in the transmission line and damage to other transformers can be generated. For these reasons, different methods such as frequency response analysis, leakage reactance measurement, short-circuit impedance measurement, dissolved gas analysis, voltage-current locus diagram, and vibration analysis (VA) have been proposed for monitoring the transformer condition, with VA being an efficient and reliable noninvasive method for detecting faults in the windings of transformers [5]. Nevertheless, its efficacy mainly depends on the identification or estimation of suitable features that exist in vibration signals [6]. Hence, a method or technique capable of extracting suitable features from vibration signals is of significant importance for early fault detection in transformers under different operating conditions. During the last few years, numerous signal processing techniques for fault detection in transformers using vibration signals have been introduced, namely, the Fourier transform (FT) and its variations such as the short-time Fourier transform (STFT), which are the most popular signal processing techniques for monitoring the condition of transformers [7; 8; 9]. In general, FT-based methods are useful for identifying or estimating the frequencies contained in a signal, which can be associated with a damage condition; however, their results can be compromised depending on the quantity of analyzed data, noise in the signal, leakage (i.e., creation of non-existent spectral components), and transient characteristics or phenomena found in the signal [10; 11].