As a part of the paper, the issue of optimizing repairs and maintenance of electrically driven gas pumping units are considered. The existing approach to the operation and maintenance of electric gas pumping units, both in the Russian Federation and abroad was described. Also, the statistics of their failures, the possibility of using a life cycle management system to assess the current state of electrical facilities and manage production assets was analyzed. In order to optimize and improve the reliability of the results of assessing the current state of this equipment, the main existing classical methods are considered, as well as the possibilities of using artificial intelligence methods. In the course of the study, models were prepared to assess the current state of electrical facilities, based on monitoring the main diagnostic (temperature, vibration) and operational (load, pressure) parameters. These models use machine learning algorithms, common in technical diagnostics: bagging and gradient boosting. The achieved accuracy in assessing the current state of electrically driven gas pumping units allows using these models as the basis for a life cycle management system. The main reason is reducing the costs and increasing the service life of electrically driven gas pumping units.