# Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits
### Convolutional Neural Network  
CNN is a multistage neural network with multiple filtering and classification stages. The network's performance is highly dependent on the total number of layers, the number of convolution kernels, and the number of neurons in the network. The construction of a CNN model can be performed by cross stacking the convolutional layer, the activation layer, the pooling layer, and the fully connected layer [11]. CNN algorithms have been developed and applied to various fault detection applications, especially in rotation systems such as bearings and gears. [PERSON] et al. demonstrated a simple two-layer convolutional model for rolling bearing fault classification by converting vibration signals into gray-scale images [12]. In this study, the vibration signals were normalized into the range of \([-1,1]\). Then, the normalized data were converted to gray-scale intensity of each pixel in the corresponding image. Using the same approach, [PERSON] et al. converted multi-sensor vibration signals into a single image file [11]. Instead of using a gray-scale image, [PERSON] et al. transformed the signals from three channels into a color image. Each channel corresponded to one of three colors: red, blue, and green (RBG). They then combined the three colors to produce RBG-colored images. The features of the color image enlarged the differences between the different fault signals and increased the fault detection accuracy. Instead of directly using vibration signals, applying a signal processing method also increases the diagnostic accuracy. [PERSON] et al. demonstrated a 2D CNN process for bearing condition classification by applying Fourier transform to acceleration signals [13]. [PERSON] et al. presented a novel energy-fluctuated multiscale feature mining approach based on a wavelet packet energy (WPE) image to perform a 2D CNN for spindle-bearing fault diagnosis [14]. The WPE transfer separated the signals into a series of frequency subspaces without losing the energy of the frequency components and greatly enlarged the vibration frequency characteristics. Furthermore, [PERSON] et al. compared the effectiveness of several time-frequency analytical methods, including the short-time Fourier transform, the wavelet transform, and the Hilbert-Huang transform [15]. Two-dimensional CNN, as a deep-learning algorithm, shows promising diagnostic accuracy. However, 2D CNN was initially designed for image analysis; it shows some limitations in processing vibration signals because vibration signals are usually presented as time-domain 1D data. To overcome this problem, 1D CNN has been applied to machine failure detection. One-dimensional CNN was first demonstrated in 2015 to analyze electrocardiograms and to detect human activity signals. Meanwhile, 1D CNN was also applied to bearing/gear fault detection. Using vibration signals is the most common approach to performing 1D CNN in bearing fault detection. [PERSON] et al. used raw vibration signals as the input data to train a 1D CNN for bearing fault detection [16]. Studies also showed that real-time and rapid failure detection could be achieved by monitoring vibration signals and processes in 1D CNN [16; 17]. Besides monitoring vibration signals, researchers are also looking to use other mechanical signals for 1D CNN-based classification. For example, [PERSON] et al. used the motor's current data to train a 1D CNN for real-time condition monitoring [18]. Similarly, [PERSON] et al. used the motor's current data as the input and developed an analytical model for inter-turn fault diagnosis by combining the 1D CNN and long short-term memory network [19]. Both 1D and 2D CNNs have shown high accuracy in classification and diagnosis. CNN retains more attributes from the original data. Therefore, it can reduce error or information loss during the feature extraction process. In this study, a CNN-based drill bit defect diagnostic application is demonstrated. Drilling is one of the most used machining processes.