Selection of Basic Parameters for the Diagnosis of Industrial Electrical Equipment Using Computer Technology

Abstract

Diagnostics of industrial electrical equipment (asynchronous electric motors, pumps, transformers) (EE) comes down to assessing the technical condition of the equipment. It is shown that one of the most important tasks in assessing the EE condition is to select the optimal set of diagnostic parameters and factors that characterize the equipment and affect it. This selection largely depends not only on the specific type of equipment but also on the method used. This task is poorly structured and poorly formalized in nature, which can result in an incorrect decision regarding the EE serviceability. In this regard, this paper proposes a comprehensive approach to the selection of basic parameters for the diagnosis of industrial electrical equipment using computer technology in conditions of information insufficiency (a large number of different types of parameters). This approach is based on the use of approaches to assessing the degree of interconnection (Spearman's rank correlation coefficient, associativity coefficients, sign correlation function “sign-sign”) and fuzzy logic (mixed production rules). The paper proposes a classification of diagnostic parameters and factors, as well as an algorithm for their selection. The proposed integrated approach allows one to select the most important diagnostic parameters and factors that affect the EE condition in a short time without loss of information and make scientifically sound diagnostic decisions regarding the EE serviceability.