Planning maintenance interventions
The planning of maintenance interventions has to take into account many different constraints, including availability of resources, optimising the cost of interventions, the impact on the availability of the rail network, and so on. However, utilising the latest information on the infrastructure status brings another challenge – last minute work plan changes can excessively disrupt the scheduled workload.
Within In2Smart2, several cases have been identified to address this concern. Azienda Trasporti Milanesi (linked third party of Hitachi Rail in this project), is as an infrastructure manager facing the growing demand for transportation facilities, meaning frequencies and operating hours are increased. This implies high demands on the maintenance manager who has to consider the availability of the infrastructure and its limited maintenance possibilities in the planning. Hitachi Rail developed a demonstrator to provide a decision support system delivering, among other things, an advanced optimised planning of interventions, taking the actual asset status and related risks into account. Algorithms support the complex constraint combinations, like optimal use of resources, minimisation of travel distance to the depot, minimisation of service disruptions due to corrective maintenance activities, and so on.
In a second demonstrator, Strukton Rail, as a maintenance service provider, continuously challenged to optimise daily maintenance on an operational level, is facing similar problems. The focus here is to transform planning from mid to short-term. Work preparation considers all constraints including contractual obligations and the state of the infrastructure. However, last minute changes normally arise from anticipating problems highlighted by continuous monitoring and data analytics. This implies adjusting the weekly maintenance plan due to corrective maintenance interventions or unexpected events.
The main impact of both cases is the optimisation of the maintenance planning by taking the actual asset status and the related risks into account, thus reducing the excessive costs incurred by an ad-hoc approach. This leads to a minimisation of service disruption due to corrective maintenance activities. More specifically, it facilitates the required reduction of needed possessions by 50%. Both demonstrators are supported by CEMOSA and Fraunhofer.