Reliability analysis and dynamic maintenance model based on fuzzy degradation approach

The rapid growth of population density and people demand based on railway services increases day by day. Hence, the maintenance (i.e., degradation and aging) and the decision-making processes of rail wheels are troublesome. There has been an impact of degradation stage for a system such as a repair, replacement, and inspection time. In this paper, an adaptive neural-based fuzzy inference system (ANFIS) rail wheel maintenance model is proposed using the modified spider monkey algorithm, with the parametric update. As the design of the wheel maintenance strategy, the degradation stage increases with system aging and it can be minimized using repair. In this way, this article presents the development of system reliability that is dealing with the establishment of a maintenance strategy. A higher running cost rate reduces the main contribution of the reparation cycle using an optimum maintenance model, and the locomotive wheels are employed to demonstrate the efficiency of the proposed methodology. Under optimal inspection time, the impact of system failures such as aging and degradation is determined successfully. The proposed model can able to identify the precise time when rail tracks fail to minimize the maintenance cost/time. This ANFIS and MSMA can increase the efficiency of maintenance activities and decrease the cost of maintenance in the long term. Similarly, the results demonstrate that the proposed maintenance model is more flexible, and the capability of gauge value prediction using real and estimated values is accurate.