## 1 Introduction and Related Work
These can exclude the expert knowledge of classic fundamental heat theory to do the task of estimating the temperature [16; 17; 18]. There are three approaches to machine learning methods: supervised, unsupervised, and reinforcement learning (RL). The applicability of the methods varies as per the applications and availability of the data. ML algorithms based on linear regression techniques have a low computational complexity. However, as linear regression is linear-time-invariant in nature, it does not capture the dynamics of a complex traction drive system. In the field of sequential learning tasks in highly dynamic environments, recurrent neural networks and convolutional neural networks are state-of-the-art in classification and estimation performance. [PERSON] [19] presented an artificial neural network (ANN) model to estimate the internal temperatures in the stator winding of a squirrel-cage induction motor supplied from a sinusoidal pulse width modulation inverter. The concept is to parameterize neural networks entirely on empirical data without being driven by the domain expertise. [PERSON] et al. in their work [20] explored a type of ANN technique, long short-term memories (LSTMs), to train for the real-time prediction of temperatures. The training procedure yielded a worst-case prediction error of 9-14 K which was found inferior compared to the LPTN approaches. However, the work will motivate future researchers to explore other ANN topology training methods to deal with the uncertainty of this problem. In the study conducted by [PERSON] et al. [21], recurrent residual neural networks with memory blocks and convolutional neural networks with residual connections were empirically evaluated for predicting temperature profiles in the stator teeth, winding, and yoke. The model hyperparameter search was conducted sequentially via the Bayesian optimization method. The mean squared error and maximum absolute deviation performances of both these methods could match that of LPTN-model-based identification methods. While data-driven methods are proven to be effective in predicting the temperature, the tuned parameters are not interpretable and could not be designed with the low amount of model parameters as in an LPTN model at equal estimation accuracy. As a further development, to make expert knowledge-based calibration less desirable and to account for the uncertainties in the input power losses, [PERSON] [22] proposed a deep-learning-based temperature model in which a thermal neural network is introduced, which unifies both consolidated knowledge in the form of heat-transfer-based LPTNs and a data-driven, non-linear function approximation with supervised machine learning. Supervised ML models have also been considered for online electrical parameter estimation such as rotor resistance and mutual inductance in the control system of an induction motor [23; 24]. In the presented work by the authors in [25], a simple two-layer ANN was trained by minimizing the error between the rotor flux linkages based on an IM analytical voltage model and the output of the ANN-trained model. Feedforward and recurrent networks were used to develop an ANN as a memory for remembering the estimated parameters and for computing the electrical parameters during the transient state. Machine learning applications are established as equally powerful tools in the controllers of motor drives [26; 27; 28; 29]. In the work presented in [26], a hysteresis controller behavior was trained offline to an ANN topology to generate the desired switching pattern. [PERSON] et al. [27] worked on two ANN controllers trained to control the stator current and rotor speed adaptively to capture the induction machine dynamics. Implementations of ANN as speed controllers have been presented in [28; 29]. The speed controllers were trained offline using simulation, but the weight and biases were updated online when the difference between the actual output and the targeted output of the ANN controller exceeds a preset value.