Rotary Machines Diagnosis Systems Based on Feed Forward Neural Networks

The article discusses the prerequisites for the use of artificial neural networks with direct connection for diagnosing the state of rotary machines with liquid friction bearings. During the study, an experimental setup with a multi-sensor measuring system was developed. The measuring system is based on sensors that measure different types of data. An experiment was conducted in which various types of failures were modeled. Data was processed to provide their network. Various ways of presenting data were used. The effect of various types of measurements on the accuracy of networks was investigated. The principles of preparing data sets are presented. A study was conducted of the influence of the volume of the training sample on the accuracy of the artificial neural network. The possibilities of using multisensory measurements of various nature for the diagnosis of rotary machines are shown.