Method for Assessing the Impact of Electric Energy Quality Indicators on the Technical Condition of Asynchronous Electric Motors

摘要

The paper proposes a new method for supporting the diagnostic decision-making in assessing the impact of electrical energy quality indicators (EQI) on the technical condition of asynchronous electric motors (TC of AM). This method is based on combining a heterogeneous cognitive model (HCM) with different types of vertices, fuzzy cause-and-effect relationships to obtain additional data on TC of AM; an artificial feedforward neural network (ANN) and the methodology of fuzzy cognitive modeling under conditions of measuring and expert data. The paper offers theoretical grounds for development of methods and decision support models in the diagnosis of AM (based on system-wide principles) and a generalized scheme of the proposed method. The authors considered a detailed numerical example of the developed method application for estimating the EQI on TC of AM in the oil industry. The method proposed provides obtaining the more complete and accurate data on TC of AM, as well as making scientifically based diagnostic decisions on AM serviceability.