Qualification of DC Brush Motors used in Spacecraft Missions using Logistic Regression Model - An Analytical Approach

DC brush motor undergoes rigorous performance testing to predict the failures occurring. Acceptance test levels are set with margin expected flight/operations levels and qualification test levels are set above the expected operation levels to check for the survivability of DC brush motor in space. Performance testing is important to design and integration in a planned test process in which DC brush motor were tested under actual or simulated mission profile environments to disclose design deficiencies and to provide information on failure modes and mechanisms. Statistical models are helpful in this process. The Logistic Regression Model is the commonly used parametric statistical models and is one of the statistical techniques that are used for analyzing and predicting performance with binary outputs. The objective was to develop Logistic Regression Model and is designed to examine the categorization of dependent variables in the quantitative analysis for the performance of DC brush motor. The work carried out demonstrates the development of the Logistic Regression Model for performance prediction of the DC brush motors for deploying unfurlable antenna. The methodology includes performance test data and transforming the dependent variables into binary digits. And to estimate the probability of success of DC brush motor. The model developed is useful in the GO-NO-GO decision making with regard to the DC brush motors which is one of the critical components in the space module. This paper adds value to the decision making process through statistical validation using Logistic Regression Model. This Model helps the decision makers in qualifying the components based on performance tests. The Model developed shows the working condition of motor can said to be good since the model fit is good. The reliability performance of the DC brush motor in the working condition is significantly good.