## 5 Conclusions  
In BSMs, rotor temperature is a crucial variable for detecting ITFs based on exciter field current estimation. As the machine heats up, the rotor's resistance increases, necessitating a rise in the rotor field voltage to maintain a constant field current and reactive power. Consequently, the exciter field current must also be increased.  
Previous research did not consider the rotor temperature as a variable in the development of the online model-based fault severity estimation algorithm for BSMs. This omission is significant, as the presented results indicate that the exciter excitation current varies by 0.75% for every 10 \({}^{\circ}\)C increase in temperature. This could result in errors exceeding 5% in an industrial machine, where the normal operating temperature range can vary from 20 \({}^{\circ}\)C in cold conditions to 100 \({}^{\circ}\)C in warm conditions. This fact implies that the algorithm could only detect faults from 5% fault severity or higher if unwanted trips are to be avoided.  
To enhance the accuracy of the detection method, an alternative online model has been developed for detecting ITFs in BSMs, incorporating the machine's temperature. Given the difficulty of measuring rotor temperature, the use of stator temperature was evaluated as an alternative, yielding satisfactory results.  
To validate the equivalence of using rotor versus stator temperature, the variation in the excitation current with different temperatures was compared in several laboratory tests, using 400,000 operational points for training the neural network.  
The results obtained in this paper indicate that the DNN using stator temperature as an input can estimate the exciter field current with errors below 0.5%. Consequently, ITFs with a severity of approximately 0.5% or higher can also be detected.  
However, this study leaves the gates open to future research, which should focus on carrying out tests with known interturn faults in the brushless generator. Thus, the DNN developed for this article could be used to verify the accuracy of the model as a function of the severity of the fault.  
Conceptualization, R.P. and C.A.P.; methodology, R.P., K.M. and C.A.P.; software, R.P. and E.R.; validation, R.P., K.M. and E.R.; formal analysis, J.M.G. and C.A.P.; investigation, R.P. and K.M.; resources, C.A.P.; data curation, R.P.; writing--original draft preparation, R.P. and J.M.G.; writing--review and editing, J.M.G., R.P., K.M., E.R. and C.A.P.; visualization, K.M.; supervision, C.A.P.; project administration, C.A.P.; funding acquisition, C.A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 13: Prediction of the excitation current using a DNN.  
Data Availability Statement: The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.  
Conflicts of Interest: Author [PERSON] was employed by the company ESELEC Ingenieros. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.