Infrastructure Smart Maintenance

Infrastructure Smart Maintenance

Infrastructure Smart Maintenance is a key topic of Shift2Rail’s Research and Innovation Programme, as laid out in the Shift2Rail Multi-Annual Action Plan. It will lead to a reliable and performing infrastructure, supporting the reduction of infrastructure maintenance costs, such as simplified procedures or automation, and importantly, the prediction of failures to prevent their occurrences. This topic is addressed within Shift2Rail’s framework, with the involvement of infrastructure managers and technology/service providers in order to achieve harmonised processes in the EU, so that the final customer can enjoy a reliable, safe and competitive travel experience. The Intelligent Asset Management Strategies Technical Demonstrator (TD) 3.8 addresses two issues – firstly, the definition of concepts for intelligent maintenance planning and decision support; and secondly, a work stream leading towards advanced tools and equipment, supporting the execution of maintenance.

The first aspect is carried out in a strong conjunction with the two other related technical demonstrators on Dynamic Railway Information Management System (TD3.6) and Railway Integrated Measuring and Monitoring System (TD3.7), forming together the Intelligent Asset Management pillar. These two other demonstrators focus on data acquisition and analytics, more precisely, monitoring asset status and understanding asset behaviour.

The Shift2Rail Research and Innovation Programme is implemented through intertwined co-funded projects with the participation of Shift2Rail Members or other partners that adhere to the same technical goals and contribute to the achievement of the demonstrators. Below we highlight two relevant cases from Shift2Rail’s In2Smart2 project managed by a consortium of Shift2Rail Members and the work carried out in the complimentary project, STREAM, managed by non-Shift2Rail Members, implementing TD3.8. We highlight the research and innovation work achieved by scientists and engineers around Europe for effective logistics, undertaking maintenance actions and advanced tooling, setting the basis for the technical demonstrators up to Technology Readiness Level (TRL) 7.

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.

Robotisation

In In2Smart2 the development of robotisation focuses strongly on work execution. With Network Rail, the idea is to develop a generic robot platform to be used in the rail environment. The main idea is to demonstrate the command and control of a small-wheeled vehicle with robotic maintenance capabilities, designed to operate during track possessions. This work is fundamental to the future of effective robot use. The system vision is to operate as an interchangeable plug-and-play platform with robotic features, enabling vehicles to be deployed for various inspection and maintenance activities.

In order to make developments future-proof, the use of the Robot Operating System (ROS – a flexible framework for building robot software) ecosystem has been adopted. It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behaviour across a wide variety of robotic platforms.

This has led to the idea to examine whether the ROS-ecosystem can be applied to existing on-track machines. This has inspired the definition of an Open Call, addressing this as a specific topic.

STREAM Project

The STREAM consortium brought together high-tech developers and railway companies to provide two methods for improving competitiveness in railway maintenance applications:

1) The development of a control platform (OTA3M) adapted to existing railway excavators by using sensors, hydraulic actuators and software that allow excavators to perform autonomous multi-purpose operations enabling safe worker-machine collaboration. The OTA3M autonomously controls the movement of the excavator along the tracks and introduces the autonomous capability to manipulate heavy components by relying on motion/force controls, obstacle detection and collision avoidance.

2) The deployment of a modular active exoskeleton (MMPE) to reduce the risk of injury by assisting workers with heavy tasks. The MMPE is tailored to track workers to reduce the risk of lumbar injury by reducing the biomechanical load for a variety of manual tasks, by predicting human intent. It uses human activity recognition, proprioceptive sensors and control strategies, while reducing workers’ effort by applying specific forces synchronized with the musculoskeletal system.

Both methods are developed on real use cases to bring the technology close to the market. Development of these technologies will take into account safety and ethical regulations, and applicable technical standards.

The impact of these developments are improved health and safety for track workers thanks to the avoidance of direct injuries, as well as the prevention of long-term health issues.

Concluding observations

Based on the original plans of the Shift2Rail Programme, the Calls for Proposals for the Shift2Rail Members covered the ideas originally foreseen, leading to tangible and practical results. The Call for non-Members, which was published at a later stage, has benefited from the experience in robotisation developments to date.

Although not originally foreseen, an additional topic on the use of exoskeletons in a rail environment was inspired by the recognition of the potential of these wearables. The anticipation of these developments are demonstrating that a long-term programme like Shift2Rail can be agile enough to take on new ideas.

Authors: Henk Samson (Strukton), with contributions from Christian Di Natali (IIT), Ute Gläser (Fraunhofer), José Solís Hernández (Cemosa), Giorgio Travaini (Shift2Rail), and Sebastien Denis (Shift2Rail).