## 5 Results
### Long Short-Term Memory  
When dealing with small datasets, particularly gear problems in DC motors, LSTM models encounter several challenges. Firstly, due to their large number of parameters, LSTMs are prone to overfitting, potentially memorizing training examples rather than generalizing well to unseen data. This challenge is especially pertinent in applications like diagnosing gear problems in DC motors, where the limited dataset may need to capture real-world conditions' total variability adequately. Consequently, the model's ability to generalize to unseen instances may need to be improved, leading to suboptimal performance. Additionally, the capacity of LSTMs to learn complex patterns is constrained by the scarcity of training examples, further exacerbating the risk of overfitting and hindering the model's ability to capture the underlying dynamics of gear faults. Moreover, small datasets often contain higher noise levels relative to the signal, amplifying the challenge of separating meaningful patterns from noise during training. Tuning hyperparameters also becomes challenging in this scenario, as there are limited data to estimate their impact on performance reliably. Finally, the risk of data leakage is heightened with small datasets, as inadequate partitioning between training and validation sets may lead to overly optimistic performance estimates. While the challenges mentioned are significant, the performance of the LSTM model can be visually evaluated through the generated plots, showcasing the predicted values compared with the actual values over time, as shown in the generated graphs in Figure 7. Here, 75% of the data are used for training and 25% are reserved for testing purposes.  
Drawing inspiration from challenges in aviation prognostics [19], this study evaluates LSTM autoencoders for prediction, demonstrating their potential to enhance prognostic capabilities despite the limited availability of labeled data. However, LSTM autoencoders encounter significant hurdles, particularly in scenarios involving small datasets such as DC motor gear problems. Their intricate architectures, laden with parameters, increase the risk of overfitting, potentially impeding generalization. This challenge is pivotal in gear problem diagnosis, where limited datasets may inadequately capture real-world conditions, affecting performance. Furthermore, the scarcity of training examples restricts the autoencoder's ability to discern fault patterns. Small datasets often harbor elevated noise levels, complicating pattern extraction during training. Additionally, hyperparameter tuning becomes challenging due to insufficient data for accurate estimation. Improper partitioning between training and validation sets heightens the risk of data leakage, resulting in overly optimistic performance estimates. Despite these obstacles, visual inspection of  
Figure 7: Comparison of LSTM predictions and actual values for training and testing data in gear fault diagnosis for DC motors.  
plots remains crucial for evaluation, facilitating comparison of predicted values over time and displaying predicted values alongside their corresponding actual values over time, as depicted in the graphs generated in Figure 8.