A comparative analysis of construction equipment failures using power law models and time series models

Abstract

Construction equipment reliability is critical to the contractors in heavy construction works. However the equipment reliability is influenced by harsh working environments, tough working conditions and varying management practices. Reliability analysis on field data of equipment failures provides meaningful insight into the failure patterns and causes. Prediction models on reliability metrics can also be established to forecast the equipment reliability performance in the planning horizon. In this paper, classical power law models are compared with time series models in terms of their performance in reliability forecasting of construction equipment. Through experimentation on a large number of field data, it is found that generic time series models based on predictive mining algorithms can better capture the complexities of equipment reliability and identify the underlying trends, patterns and rules for decision support. Classical statistic-based power law models demonstrate better performance in model simplicity, ability of modeling subsystem reliability with minimum failure data. Proper selection of prediction models for reliability analysis can help the contractor to optimize the preventive maintenance and overhaul program by turning unscheduled maintenance actions into scheduled ones to minimize impact on project progress.