###### Abstract  
This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are transformed into their frequency components and then employed as inputs to a CNN model, which is trained to detect patterns indicative of drill bit failure. XAI algorithms are then employed to generate attention maps, highlighting regions of interest in the CNN. By scrutinizing these maps, engineers can identify critical frequencies associated with drill bit failure, providing valuable insights for maintenance and optimization. This method offers a transparent and interpretable framework for analyzing vibration data, enabling informed decision-making and proactive maintenance strategies to enhance drilling efficiency and minimize downtime. The integration of XAI with CNNs facilitates a deeper understanding of the root causes of drill bit failure and improves overall drilling performance. rock drilling; bit wear; signal analysis; convolutional neural network; explainable artificial intelligence; gradient class activation mapping +
Footnote †: journal: Medical  
A drilling operation is a dynamic process that involves the high-speed rotation of a drill bit and its interaction with geological formations. These interactions generate vibration signals that contain valuable information about the condition of the drill bit, the effectiveness of the drilling process, and the properties of the surrounding rock formations [9]. Drill bit wear is a major concern in drilling operations. It is a gradual process affected by factors like drilling parameters, rock properties, and operational conditions [10]. Detecting and assessing drill bit wear early on is crucial to avoid expensive downtime, minimize maintenance costs, and maintain operations [11; 12]. Vibration analysis is a conventional method for detecting subtle changes in the vibration patterns of drill bits. These changes could indicate the beginning of wear or degradation. By analyzing the frequency, amplitude, and other characteristics of these signals, deviations from normal operations can be detected [13]. These deviations may appear as changes in vibration harmonics, increased levels of high-frequency noise, or alterations in the frequency spectrum of the vibration signal [14]. Signal analysis techniques such as the fast Fourier transform (FFT) and wavelet transform (WT) are commonly used in various studies. The FFT breaks down time-based waveforms into different frequency components [15] while the WT analyzes signals in both the time and frequency domains simultaneously [16]. [PERSON] et al. [17] conducted an analysis of vibration signals from a downhole drill string. They used a three-axis accelerometer to measure vibration signals during drilling in an oil well. The time-frequency characteristics of the signal were then analyzed using both FFT and short-time Fourier transform (STFT). This analysis proved effective in identifying harmful vibrations associated with drilling into igneous rock. [PERSON] and [PERSON] [18] applied signal analysis to study the relationship between drill signals and bit wear by analyzing measurement-while-drilling (MWD) data. Their analysis included both time domain statistical analysis and frequency spectrum analysis of the signals. They were able to identify specific frequency bands that were responsive to both bit wear and drillability. Similarly, [PERSON] and [PERSON] [19] discovered that acoustic emission monitoring techniques are effective in optimizing diamond core drilling performance and detecting changes in drilling conditions.