A New Decision Support System (DSS) for Optimization of Rail-Tracks Maintenance Planning, Based on an Adaptive Neuro-Fuzzy Inference System (ANFIS)

It is well-known that maintenance planning affects, in general, the life of the structures, material wear, and quality of service. In particular, the maintenance of rail-tracks affects the traffic volume as well, and therefore it is an important issue for the management of a railway system: then, an accurate maintenance planning is necessary to optimize resources. The condition of railways is checked through special diagnostic trains. Because of the vast amount of data that these trains record, it is necessary to analyze this data through an appropriate Decision Support System (DSS). However, the most up-to-date DSS’s, like Ecotrack, are based on a binary logic with rigid thresholds and complicated algorithms with a large number of rules that restrict their flexibility in use; in addition, they adopt considerable simplifications in the rail-track deterioration model. In this paper we have implemented a neuro-fuzzy inference engine for a DSS to overcome these drawbacks; based on the fuzzy logic, 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 precise rail-track deterioration models. The results obtained with the proposed model have been clustered through a fuzzy procedure, in order to optimize the maintenance schedule, thus grouping the interventions in space and in time