###### Abstract
They attached acoustic emission sensors to both the drill and the rocks, recorded emitted acoustic signals during drilling, and analyzed the waveforms. Their observations revealed that as wear accelerates, the amplitudes of the acoustic emissions decrease. Another study by [PERSON] et al. [20] used a GoPro camera to capture drilling sounds and applied signal processing techniques, such as time series analysis, FFT, and WT, to assess button bit failure. The results of this study demonstrated the usefulness of drilling sounds in detecting the condition of drill bits. However, signal analysis methods have some limitations. Traditional vibration analysis relies heavily on the expertise and understanding of the analyst, making the interpretation of vibration signals subjective and susceptible to human errors [21; 22]. In today's complex industrial environment, where machinery and structures are becoming increasingly intricate, conventional vibration analysis may struggle to effectively handle this complexity [23]. This can lead to inaccurate detection and diagnosis of faults. Furthermore, the sheer volume of vibration data generated can be overwhelming, leading to time-consuming tasks related to data analysis and management. It is crucial to tackle these challenges to enhance the accuracy and efficiency of vibration analysis techniques. By integrating advanced technologies like artificial intelligence and machine learning, we can overcome these limitations and create more reliable and effective methods for fault detection and diagnosis. Machine learning algorithms are capable of handling complex data patterns and efficiently processing large amounts of data, enabling real-time monitoring [24]. [PERSON] and [PERSON] [14] utilized a CNN algorithm to detect bearing faults using vibration and acoustic signals. Their approach involved several steps, including data preprocessing, STFT, and feature extraction. The model was evaluated on four distinct bearing conditions and achieved a classification accuracy of 100%. In another study, [PERSON] et al. [25] developed artificial neural network (ANN) models to predict the geomechanicalproperties of sedimentary rock types using acoustic signal dominant frequencies. A microphone was used to record the drilling sounds during the core drilling operations in the laboratory. The ANN model was efficient in determining the physicochemical rock properties. [PERSON] et al. (2016) proposed a method to detect drill bit failure in rotary percussion drills using a one-dimensional convolutional neural network (1D CNN) with drill vibration as the input data. The 1D CNN model was evaluated on five drilling conditions: normal, defective, abrasion, high pressure, and misdirection. The model achieved a classification accuracy of 88.7%. CNNs are highly effective for tasks such as fault detection. However, understanding the reasons behind their predictions can be challenging. This lack of interpretability is a crucial issue in applications where transparency, accountability, and the ability to explain decisions are essential. It can also hinder the identification and resolution of biases or errors in the model's predictions ([PERSON], 2017). To address this, explainable artificial intelligence (XAI) can be applied to make deep learning models transparent to users. XAI aims to make the decision-making process of AI systems understandable and transparent to humans by employing diverse techniques. A notable method involves highlighting the specific regions within an input that contribute most significantly to the model's predictions ([PERSON], 2017). In this study, we propose a novel approach that integrates XAI techniques, specifically Gradient-weighted Class Activation Mapping (Grad-CAM), with CNNs for interpretable drill bit wear detection from vibration data. Although the integration of Grad-CAM with CNN is not a new concept, our study introduces an innovative application of this technique in the domain of drill bit wear detection.