A Decision Support System Based on Neuro-Fuzzy System for Railroad Maintenance Planning

Optimization of Life Cycle Cost (LCC) in railroad maintenance, is one of the main goals of the railways managers. In order to achieve the best balance between safety and operating costs, “on condition” maintenance is more and more used; that is, a maintenance intervention is planned only when and where necessary. Nowadays, the conditions of railways are monitored by means of special diagnostic trains: these trains, such as Archimede, the diagnostic train of the Italian National Railways, allow to observe every 50 cm dozens of rail track characteristic attributes simultaneously. Therefore, in order to plan an effective on condition maintenance, managers have a large amount of data to be analyzed through an appropriate Decision Support System (DSS). However, even the most up-to-date DSSs have some drawbacks: first of all, they are based on a binary logic with rigid thresholds, restricting their flexibility in use; additionally, they adopt considerable simplifications in the rail track deterioration model. In this paper, we present a DSS able to overcome these drawbacks. It is based on fuzzy logic and it is able to handle thresholds expressed as a range, an approximate number or even a verbal value. Moreover, through artificial neural networks it is possible to obtain more likely the rail track deterioration models. The proposed model can analyze the data available for a given portion of rail-track and then it plans the maintenance, optimizing the available resources.