Earthmoving trucks condition level prediction using neural networks. Issue 2 (6th May 2014)

• Record Type: Journal Article
• Authors: Marinelli, Marina; Lambropoulos, Sergios; Petroutsatou, Kleopatra
• Is Part Of: Journal of quality in maintenance engineering. Volume 20:Issue 2(2014)
• Page Start: 182
• Page End: 192
• Publication Date: 2014-05-06
• Subjects: Artificial neural networks -- Condition prediction -- Discriminant analysis -- Earthmoving -- Trucks; Plant maintenance -- Quality control -- Periodicals; Total quality management -- Periodicals; Total productive maintenance -- Periodicals; 658.202
• DOI: 10.1108/JQME-09-2012-0031
• Languages: English
• ISSNs: 1355-2511

Abstract

Abstract : Purpose: – The purpose of this paper is to present an artificial neural network (ANN) model that predicts earthmoving trucks condition level using simple predictors; the model's performance is compared to the respective predictive accuracy of the statistical method of discriminant analysis (DA). Design/methodology/approach: – An ANN-based predictive model is developed. The condition level predictors selected are the capacity, age, kilometers travelled and maintenance level. The relevant data set was provided by two Greek construction companies and includes the characteristics of 126 earthmoving trucks. Findings: – Data processing identifies a particularly strong connection of kilometers travelled and maintenance level with the earthmoving trucks condition level. Moreover, the validation process reveals that the predictive efficiency of the proposed ANN model is very high. Similar findings emerge from the application of DA to the same data set using the same predictors. Originality/value: – Earthmoving trucks' sound condition level prediction reduces downtime and its adverse impact on earthmoving duration and cost, while also enhancing the maintenance and replacement policies effectiveness. This research proves that a sound condition level prediction for earthmoving trucks is achievable through the utilization of easy to collect data and provides a comparative evaluation of the results of two widely applied predictive methods.