## 2 Data and Their Processing
### Data Processing and Utilization  
The dataset includes multiple experiments, each with varying motor temperature ranges. By traversing the dataset, it was found that group number 24 (hereafter referred to as Group A) has the largest range of motor rotor temperature variation, with a minimum value of 21.93 \({}^{\circ}\)C and a maximum value of 113.61 \({}^{\circ}\)C. Group number 46 (hereafter referred to as Group B) has the fewest samples, and the motor rotor temperatures are concentrated in the high-temperature range, with a minimum value of 78.08 \({}^{\circ}\)C and a maximum value of 92.91 \({}^{\circ}\)C. Table 2 records the variation range of the rotor temperature and the total sample size.  
These two groups were selected for further study. The focus is on establishing a mapping model, with rotor temperature as the prediction target and the other variables as inputs. Different preprocessing methods were applied to Groups A and B. Data description, preprocessing, and usage are illustrated in Figure 2.  
Group A contains 15,015 samples. A preprocessing method was used where every fifth sample was retained, resulting in a moderately sized dataset of 3003 samples. Given the original sampling frequency of 2 Hz, the time interval between data points is short, meaning adjacent samples have highly similar information. Since the primary goal is to predict rotor temperature, which typically does not change rapidly, a very high sampling frequency is unnecessary for analyzing its dynamics. This preprocessing method effectively reduces data redundancy while retaining sufficient information for modeling and analysis. Subsequently, 500 samples were randomly selected as the training set, and the remaining samples formed the test set. Compared to the typical 7:3 ratio, this approach allows the test set to better simulate the distribution of unknown data, providing a more realistic assessment of the model's performance in practical applications.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Range** & **Count** & **No.** & **Range** & **Count** & **No.** & **Range** & **Count** \\ \hline
2 & 23.02 & 19,357 & 27 & 87.41 & 35,361 & 59 & 47.07 & 7475 \\
3 & 6.26 & 19,248 & 29 & 75.60 & 21,358 & 60 & 49.64 & 14,543 \\
4 & 76.4  
Group B follows a similar approach. From a total of 2180 samples, every 10 th sample was retained, resulting in a new dataset of 218 samples. This small sample size effectively tests the model's generalization ability under limited data conditions. If the model performs well, this indicates a strong learning capability and can achieve high prediction accuracy even with scarce data. In practical applications, large quantity data acquisition may be of great difficulty, e.g., requires a high cost, under certain rotor temperature scenarios. Therefore, Group B aims to simulate these scenarios and test the model's performance. The ratio of the training set to the test set is 1:1, meaning that half of the data are randomly selected for training, and the rest are used to test the model's generalization ability.  
The data (Group A and B) and their description can be found at the following URL (accessed on 18 February 2025): [[https://github.com/567](https://github.com/567) ZYC/Data-Explanation-for-Electric-Motor-Temperature-Dataset]([https://github.com/567](https://github.com/567) ZYC/Data-Explanation-for-Electric-Motor-Temperature-Dataset).