Development of a Full Mathematical Model for Evaluating the Performance of an Electrically Driven Ring Spinning Machine under Drive Failure and Motor Speed Reduction
DOI:
https://doi.org/10.51699/ehmgsm75Keywords:
electric drive, ring spinning machine, mathematical model, response surface methodology, regression analysis, experimental designAbstract
The operational efficiency of modern ring spinning machines is strongly influenced by the technical condition of the electric drive system. Temporary drive failures and reductions in motor rotational speed directly affect production stability, yarn quality, and machine productivity. Existing mathematical approaches generally evaluate these factors independently or employ simplified linear relationships, which restricts the accuracy of technological performance prediction under real operating conditions.
This study develops a comprehensive second-order mathematical model for evaluating the performance of an electrically driven ring spinning machine by simultaneously considering the duration of electric drive failure and the percentage reduction in motor rotational speed. The experimental investigation was organized according to a second-order orthogonal design based on Kono planning. Two independent variables were investigated using nine experimental design points with twelve randomized replications at each point. The obtained experimental data were processed through regression analysis to establish linear, quadratic, and interaction effects between the selected technological factors.
The adequacy and statistical reliability of the developed model were verified using Cochran's homogeneity criterion, Student's significance test, and Fisher's adequacy criterion at a confidence probability of 95%. The statistical analysis confirmed that the proposed regression model adequately represents the experimental observations and accurately describes the nonlinear behaviour of the spinning process under varying electric drive operating conditions.
The developed mathematical model makes it possible to quantitatively predict productivity changes resulting from simultaneous variations in drive failure duration and motor speed reduction. The obtained response surface provides a practical tool for selecting rational operating conditions that minimise productivity losses while improving the reliability of electrically driven spinning machines. The proposed modelling approach may serve as a theoretical basis for intelligent monitoring, adaptive control, and optimisation of spinning machine operation in modern textile production systems.
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