Prognosis and Diagnosis of Farm Tractors Reliability and Availability for Maintenance Policies Using Markov – Chain Model

摘要

A sound maintenance planning is of crucial importance for farm power systems. There is a large potential in cost savings by optimizing maintenance decisions to make utilization of farm tractors more cost-efficient. Reliability and availability are fundamental attributes of organization, scheduling and operation of fleet of tractors in agricultural project of multi-farms. This paper utilizes recursive Markov chain closed–form analytical solution and condition-based maintenance model to evaluate performance of degraded multi-state system. First state of system failure is inspected, analyzed, and classified into partial, or combined or complete failure of estimating the transition matrix for the failure state. At each inspection of failure status a preventive maintenance (minor repair by replacement of parts) or corrective maintenance (major replacement of parts by complete overhaul) is performed to restore the system to "as good as new". The development of condition-based maintenance is used to signify the monitoring of machines for the purpose of diagnostics and prognostics. Diagnostics are used to determine the current status of a machine's frequency of failure (useful life) and prognostics are used to predict its dependability, availability (utility).Hence, the system of evaluation is quantified by six distinct indicators (maximum time before failure, tractor dependability, availability, frequency of failure and operating time between preventive and corrective maintenance) such that appropriate actions can be planned and taken in order to minimize the impact of equipment failure to operation. Simulation results for a dataset of three tractors (T120, C225 and B250) from two workshops of sugar plantation (Gunied and Sennar factories in Sudan) is investigated to assert the magnitude of variation between the tested variables that justify changing current maintenance policy using analysis of variation. The results indicate the applicability of Markov where comparison with condition-based maintenance is the optimal maintenance strategy for tractor high failure rate.