## 5 Discussion  
Vibrational signals provide valuable information about the operating conditions of machinery. It is crucial to identify the frequencies associated with abnormal drill bit behavior to detect faults early and prevent further damage. The interpretability of Grad-CAM  
Figure 10: Grad-CAM heat maps: (**a**) normal and (**b**) abnormal.  
Figure 9: Visualization of the Resnet-50 model’s activations from the last 10 convolutional layers using the t-SNE method. The normal drill bit is depicted in blue, while the abnormal one is shown in orange.  
enables us to identify the frequency ranges that the model considers representative of drill bit failure. The study results showed that, under normal conditions, the model focused on frequencies of 0-1000 Hz and 4000-6000 Hz. Under abnormal conditions, the model focused on frequency ranges of 2500-6000 Hz. The signal analysis method yielded similar results. Both approaches identified dominant frequencies within the 0-6000 Hz range for different conditions. Frequency analysis provided additional insight by distinguishing the acceleration peaks of each condition in more detail. However, this capability is a limitation of the Grad-CAM approach. On the other hand, Grad-CAM offers an advantage by providing interpretable visualizations directly from the neural network, eliminating the need for domain-specific knowledge and subjective analysis while accomplishing the task efficiently. In practical applications, a combined approach using both traditional signal analysis and Grad-CAM simultaneously would offer superior insights and advantages. It is crucial to have a high-accuracy model that consistently predicts drill bit conditions to effectively leverage Grad-CAM. This ensures that Grad-CAM interpretations are based on reliable predictions, enhancing the reliability and trustworthiness of the insights gained from the model. Before applying Grad-CAM, determining the most informative convolutional layer in the neural network is crucial. The t-SNE analysis becomes invaluable as it allows researchers to visualize feature representations at different layers of the network. By selecting the most discriminative layer, Grad-CAM can accurately identify regions of interest in the input data that significantly contribute to the model's predictions. The use of Grad-CAM for interpretability in drill bit failure detection represents a significant advancement in condition monitoring and predictive maintenance. Incorporating interpretable AI techniques like Grad-CAM into existing monitoring systems improves the efficiency and reliability of maintenance processes, reduces downtime, and optimizes resource utilization. Ultimately, adopting advanced analytic solutions enhances equipment reliability, cost savings, and operational excellence in industrial settings. The main limitation of the study is that it does not consider the geological conditions associated with rock formations. Geological factors have a significant impact on drilling performance and the wear patterns of drill bits. Additionally, the study does not address the variability in drilling parameters encountered during actual drilling operations. In real-life scenarios, drilling parameters often change due to various factors, making it difficult to consistently control these variables. Consequently, future research should prioritize the development of systems that can adapt to dynamic drilling conditions by incorporating geological data and accounting for fluctuations in drilling parameters. Such an approach would enhance the system's robustness and practicality in real-world settings.