Method and algorithm for identifying factors influencing the failure of electrical equipment

Abstract

Relevance: Identifying factors influencing the failure of electrical equipment is a crucial task in optimizing energy consumption and improving equipment reliability. In industrial enterprises, equipment failures negatively affect the continuity of technological processes and result in additional costs. This study focuses on identifying key factors influencing equipment failures, analyzing their interconnections, and developing methods to improve the technical condition of equipment.

Objective: The objective of this study is to identify the factors influencing the failure of electrical equipment, classify them into primary and secondary groups, and develop an algorithm for their analysis. Additionally, the study aims to propose effective methods that can be used to predict equipment failure times.

Methods: Modern analysis and modeling methods were applied to identify the factors influencing electrical equipment failures. The expert evaluation method was used to assess the degree of influence of these factors on equipment failures. A correlation matrix was constructed for an in-depth analysis, enabling the identification of interrelations between factors. Furthermore, a dedicated algorithm for factor evaluation and analysis was developed.

Results: The study identified primary and secondary factors influencing electrical equipment failures. Based on the results of expert evaluation, the most significant factors contributing to equipment failures were determined. Using the correlation matrix, the interrelation coefficients of the factors were calculated, allowing for a more precise assessment of their impact on the failure process. The algorithm developed during the study demonstrated high efficiency in analyzing the technical condition of electrical equipment and predicting its failures. This algorithm serves as a vital tool for ensuring the continuity of technological processes. The findings of this study can be applied to improve maintenance planning for electrical equipment, reduce failure risks, and enhance the efficiency of industrial processes.