Prediction of restoration factor for various maintenance types of railway systems using analytical hierarchy process

Abstract

Purpose For repairable systems (RSs), reliability estimation is generally performed using virtual age models. Virtual age models consider the effect of maintenance actions by reducing system age using restoration factor (RF). RF is generally estimated from system failure data using various statistical methods. However, RSs such as railway systems experience various types of maintenance actions at different times during their life cycle. To consider all these different types of actions, we need multiple RFs in the virtual age model. As failure data are limited, the estimation of so many parameters becomes a complex problem and it can lead to erroneous inferences. These RFs are representative of effects of maintenance activities on the system. Therefore, these can be predicted from the information about the maintenance actions performed on the system. The paper aims to discuss these issues. Design/methodology/approach The paper considers different types of maintenance actions to predict RF of the system. These maintenance actions involve the replacement of components at some level of assembly. Each component in an assembly has its own impact on assembly restoration. RF for assembly/systems can be obtained by aggregating effects of multiple component replacement using analytical hierarchy process . The RF values obtained for different types of maintenance actions are then used to calculate the virtual age of the system at different failure points. Using these virtual age failure points, suitable distribution is fitted and parameters are estimated. The distribution and parameters provide information about reliability of the system at any point of time. Findings This paper provides an easier approach that gives different RFs for different types of PM and CM. To calculate RFs, it considers the impact of maintenance actions performed as well as the impact of the component on which they are performed. It is simpler and gives more consistent results than other approaches, which estimate RF using different statistical methods. Originality/value This paper provides an alternative approach to predict RF parameters instead of estimating these parameters using statistical methods. Estimation of parameters using different statistical methods is complex in nature and gives erroneous and inconsistent results. The approach given in this paper is simpler and gives more reliable results. This approach can be useful in estimating parameters for RSs when failure data are limited.