• Title of article

    Development of Life Span Forecasting Model for KHS DMG-VF84 Bottling Line System

  • Author/Authors

    ekeoma, cg abia state polytechnic - department of mechanical engineering, Aba, Nigeria , aririguzo, jc michael okpara university of agriculture - department of mechanical engineering, Umudike, Nigeria , nwadinobi, cp abia state university - department of mechanical engineering, Uturu, Nigeria , nwankwojike, bn michael okpara university of agriculture - department of mechanical engineering, Umudike, Nigeria

  • From page
    863
  • To page
    866
  • Abstract
    A quadratic model was developed in this study to predict equipment life span for a preventive maintenance planning of KHS DMG-VF84 bottling line system. This life span forecasting model for KHS DMGVF84 bottling line system predict equipment life span at an instant of time. The six key predictors that were significant in the developed model are: Availability (A), Reliability (R), Mean time to Repair (T), Failure rate (F), Operational time (O) and Mean time before failure (B). The model utilized the polynomial method to predict the end of life of the bottling line system. The test data showed that the mean absolute percentage error for this model is 7.5% and has the ability to predict life span of the bottling line system with a good degree of accuracy of 79.65% with ± 0.20% error and the coefficients of determination R^2 for the developed model is 0.7965. This indicates that the relationship between the dependent variable and the independent variables of the developed model is good and the predicted values from a forecast model fit with the real-life data as well. Therefore, maintenance professionals should adopt this model for accurate estimates, to enable good detection of possible failures in production machineries.
  • Keywords
    regression model , Time to failure , preventive maintenance planning , bottling line , Availability
  • Journal title
    Journal of Applied Sciences and Environmental Management
  • Journal title
    Journal of Applied Sciences and Environmental Management
  • Record number

    2728900