Paper Title
Prediction Of Load-Haul-Dumper (LHD)Machine Performance Characteristics Using Feed-Forward-Back-Propagation Ann Model

Abstract
Faced with intense global competition and to enhance the expected targets of production and productivity, every industry is constantly looking to maintain the equipment in an efficient and effective manner. Hence, it is more essential to predict the performance of the equipment. By keeping this in view, the present research work deals with an artificial neural network (ANN) modeling of the underground mining equipment such as load haul dumper (LHD) to predict the percentage of reliability, availability and preventive maintenance time schedules. The input data for network training was collected from the field visit. The prediction model was developed based on feed-forward-back-propagation Levenberg-Marquardt training algorithm. The performance of the equipment was validated by comparing computed data sets with predicted data. The predicted outcomes demonstrate that the ANN model provided good agreement with the computed data with high accuracy. Keywords - LHD, ANN, Performance, Reliability, Availability and Preventive maintenance.