However, preceding algorithms fail to account for the impact rotor winding temperature may have on field current estimation. During machine operation, a notable increase in rotor winding temperature occurs, gradually causing a decline in the field current. It is interesting to highlight that, while extensive studies have explored field current estimation, limited research investigates temperature-induced alterations in these values. In [19], field current and field winding temperature are estimated using a machine model, and then the estimates are compared with the actual currents measured in the stator and adjusted through a correction mechanism using a Kalman filter, demonstrating efficacy in simulations. In [20], field current and temperature estimation is achieved through the DC link current, initially computed via an algorithm and afterward fed back to the controller and compared with the measured value. The resulting error facilitates temperature estimation refinement, thereby improving both field and DC link current estimates. Furthermore, due to the difficulty of accurate modeling posed by SMs combined with recent advancements in computational learning, contemporary studies explore methodologies such as neural networks for identifying faults within the field winding. Works such as [18; 21] collect healthy machine data alongside post-fault data to use these data for early fault detection. Nonetheless, these methods do not work in the absence of real-time consideration of BSM operating temperatures, as situations may arise where a fault between turns in a cold machine exhibits field current values like those of a healthy machine operating at high temperatures. Taking into account this difficulty, the main objective of this work is to demonstrate and verify the influence of temperature on the exciter excitation current estimation algorithm. This will substantially improve the results studied in previous works [18], where a neural network model was presented that allowed the estimation of the excitation correlation of a brushless machine but did not consider the rotor temperature. Considering how the rotor temperature affects the excitation current of the machine, the results obtained by taking into account the machine temperature as input to the model will be much closer to the real machine measurements. The second objective is to validate the use of stator temperature instead of rotor temperature in the estimation algorithm. This is a very important validation because the stator temperature can be measured during operation in industrial BSMs, where it is not possible to measure the rotor temperature. For this purpose, this paper proposes using DNN architecture algorithms. It was decided to use DNNs to estimate the excitation current because the introduction of artificial intelligence in the field of diagnosis of synchronous machines is on the rise, as can be seen in several recent research works [22; 23; 24]. The developed DNN uses voltage, active and reactive power, and temperature (_U_, \(P\), \(Q\), and _\(\theta\)_) as inputs of the model to estimate the field excitation current of the BSM. The proposed method was developed using a special laboratory setup, firstly to collect all the necessary data to train the DNN under healthy conditions. In Section 2, the influence of temperature is analyzed in different excitation systems of synchronous machines (SMs). It also highlights the importance of considering temperature as an input in developing the theoretical excitation current estimation model of a BSM. The proposed method and the steps to develop the estimation method are discussed in Section 3. Then, Section 4 describes the experimental setup and the experimental tests performed on it under healthy conditions. Finally, Section 5 summarizes the paper's conclusions.