On fault classification in rotating machines using fourier domain features and neural networks

The paper addresses the problem of classifying mechanical faults in rotating machines. In this context, three operational classes are considered, namely: normal (where the machine has no fault), unbalance (where the machine load has its weight not equally distributed), and misalignment (where the rotor and machine axes are dislocated from its natural concentric position). A large dataset consisting of 606 distinct scenarios is developed for system training and testing, along with a preprocessing strategy that improves data distribution among the three classes considered. A classifier based on an artificial neural network is described, achieving a global accuracy rate of 93.5%.