Detecting Most Influential Parameters in High Voltage Induction Motor Failure using Logistic Regression Analysis

Abstract

This study identifies the most influential factors of rotating machines or induction motors that are commonly used in the industry such as oil and gas industry. The data gathered in the industry, especially for High Voltage Induction Motors (HVIM) in the plant commonly does not fully utilize to detect the most influential maintenance factor. This required advance and latest technology to convert the data into more usable and estimate the probability of failure efficiently. A predictive maintenance solution can be used to solve data-driven problems like the complexity to compute, taking longer time to analyze, and difficulty in using big data features. Logistic Regression Analysis (LRA) methods can be used to determine the most influencing factor (MIF) of maintenance of high voltage induction motors. The MIF of HVIM maintenance obtained in the study is vibration, temperature, and power factor with an R2 value of 0.9993888. It is shown that the R2 value was high and significant. The HVIM maintenance prediction based on MIF is suited for industrial applications due to its fitness in using industrial data for analysis. As a result, it suited the existing industrial maintenance for predictive purposes and can be further developed in the future.