Predicting construction plant breakdown time using time series modelling

Construction plant breakdown affects projects by prolonging duration and increasing costs. Therefore, prediction of plant breakdown, as a precursor to conducting timely maintenance works, cannot be underestimated. This paper thus sought to develop a model for predicting plant breakdown time from a sequence of discrete plant breakdown measurements that follow non‐random orders. An ARIMA (1,1,0) model was constructed following experimentation with exponential smoothening. The model utilised breakdown observations obtained from six wheeled loaders that had operated a total of 14,467 hours spread over a 300‐week period. The performance statistics revealed MAD and RMSE of 5.03 and 5.33 percent respectively illustrating that the derived time series model is accurate in modelling the dependent variable. Also, the F‐statistics from the ANOVA showed that the type and frequency of fault occurrence as a predictor variable is significant on the model's performance at the five percent level. Future work seeks to consider a more in depth multivariate time series analyses and compare/contrast the results of such against other deterministic modelling techniques.