Artificial neural networks of technical state prediction of gas compressor units electric motors

Abstract

Issues of engineering effective and reliable systems for on-line diagnostics of electric motors of electrically driven compressor stations are considered. The paper provides failure statistics for the most critical gas-transport systems’ units – electrically driven gas-compressor stations. Artificial neural networks methodology and architecture were developed to obtain prediction models of MW electric machines. Examples of neuro-fuzzy prediction of synchronous machines stator winding performance and service life are given. Selected network tests, the Box-Jenkins fuzzy model, models of the analysis technique of spectral components dynamics, current magnitude and stator temperature prediction are received. Based on results of comparative analysis of anticipated conditions of electric machines for main gas transport with due regard to various operational factors of electrically driven gas-compressor units, recommendations on application of the artificial neural network method have been drawn up.