Automatic Fault Diagnosis of Drills Using Artificial Neural Networks

Machine fault diagnosis (MFD) recovers all the studies that aim to detect faults automatically in the machines. This study aims to develop a sound based MFD system for drills using the pattern recognition techniques such as principal components analysis (PCA) and artificial neural networks (ANN). The sound signals emitted by healthy and faulty drills are obtained and analyzed. Unlike the conventional methods that focus on the time domain, we explore here the effectiveness of the frequency domain components and demonstrate the ineffectiveness of the time domain based analysis of the sounds produced by the drills. The power spectrum components of the sounds are extracted before using PCA for the purpose of dimensionality reduction and redundancy removal. The first principal components are then selected and given to a neural network based classifier in order to perform the diagnosis. The results show that the proposed method can be used for the sounds based automatic diagnosis system.