Drill Fault Diagnosis Based on the Scalogram and Mel Spectrogram of Sound Signals Using Artificial Intelligence

In industry, the ability to detect damage or abnormal functioning in machinery is very important. However, manual detection of machine fault sound is economically inefficient and labor-intensive. Hence, automatic machine fault detection (MFD) plays an important role in reducing operating and personnel costs compared to manual machine fault detection. This research aims to develop a drill fault detection system using state-of-the-art artificial intelligence techniques. Many researchers have applied the traditional approach design for an MFD system, including handcrafted feature extraction of the raw sound signal, feature selection, and conventional classification. However, drill sound fault detection based on conventional machine learning methods using the raw sound signal in the time domain faces a number of challenges. For example, it can be difficult to extract and select good features to input in a classifier, and the accuracy of fault detection may not be sufficient to meet industrial requirements. Hence, we propose a method that uses deep learning architecture to extract rich features from the image representation of sound signals combined with machine learning classifiers to classify drill fault sounds of drilling machines. The proposed methods are trained and evaluated using the real sound dataset provided by the factory. The experiment results show a good classification accuracy of 80.25 percent when using Mel spectrogram and scalogram images. The results promise significant potential for using in the fault diagnosis support system based on the sounds of drilling machines.