Using Artificial Intelligence to Determine the Type of Rotary Machine Fault - Zuth, Daniel; Marada, Tomas (Institute of Automation and Computer Science, Brno University of Technology, 2018)
The article deals with the possibility of using machine learning in vibrodiagnostics to determine the type of fault of rotating machine. The data source is real measured data from the vibrodiagnostic model. This model allows simulation of some types of faults. The data is then processed and reduced for the use of the Matlab Classication learner app, which creates a model for recognizing faults. The model is ultimately tested on new samples of data. The aim of the article is to verify the ability to recognize similarly rotary machine faults from real measurements in the time domain.
Keywords: Vibrodiagnostics; Neuron Network; Classification Learner; Machine Learning; Matlab; Industry 4.0; Classification Method; Static Unbalance; Dynamic Unbalance