Report

Teaser, summary, work performed and final results

Periodic Reporting for period 3 - IN2SMART (Intelligent Innovative Smart Maintenance of Assets by integRated Technologies)

Teaser

IN2SMART was born in the context of a growing demand for a step change in asset management to be delivered through innovative technologies, new economic possibilities, and enhanced legislative standards in the rail sector. It wants to contribute to create new and optimised...

Summary

IN2SMART was born in the context of a growing demand for a step change in asset management to be delivered through innovative technologies, new economic possibilities, and enhanced legislative standards in the rail sector. It wants to contribute to create new and optimised strategies, frameworks, processes and methodologies, tools, products and systems for the implementation of a step change in risk based, prescriptive and holistic asset management in the rail sector. To do this the project is structured in the three former Technological Demonstrators:
• TD3.7 Railway Information Measuring and Monitoring System (RIMMS) that focuses on asset status data collection (measuring and monitoring), processing and data aggregation producing data and information on the status of assets;
• TD3.6 Dynamic Railway Information Management System (DRIMS) that focuses on interfaces with external systems; maintenance-related data management and data mining and data analytics; asset degradation modelling covering both degradation modelling driven by data and domain knowledge and the enhancement of existing models using data/new insights;
• TD3.8 Intelligent Asset Management Strategies (IAMS) that concentrates on decision making (based also but not only on TD3.6 input); validation and implementation of degradation models based on the combination of traditional and data driven degradation models and embedding them in the operational maintenance process based upon domain knowledge; system modelling; strategies and human decision support; automated execution of work.
Asset Management and, in particular, maintenance can be improved by achieving predictive Asset Management strategies. The keyword is “smart data”: analyzing the huge volumes of data provides the basis for efficient and risk-based decision-making. Smart data can provide infrastructure operators, system suppliers and construction companies with comprehensive information.
In particular, new predictive approaches could make possible for operators to be well aware of Asset Management and maintenance needs before failure.
Therefore, an intelligent infrastructure should be equipped not only with a range of static and mobile autonomous monitoring technologies/sensors, which are able to communicate with each other, but also with tools and predictive algorithms, degradation laws and models to provide a running commentary of the infrastructure current and predicted status to achieve a reliable railway infrastructure.
In this context, the IN2SMART project aims at contributing to the development of an Intelligent Asset Management System (IAMS), identifying the main ‘building blocks’ and their interactions, showing how the use of big data and predictive analytical techniques could foster the optimization of Asset Management and the prolongation of asset lifetime.

Work performed

The decision support system contributes to the prevention of costly failures and supports operational Asset Management and maintenance decision making, especially with regards to interventions planning.
With regards to the main results achieved so far we would highlight, in a not exhaustive way, some Business case examples currently developed inside the project:
• Use Case – False Track Occupancy mitigation: The business case consists in a decision support system for the risk-based scheduling of Track Circuits’ predictive maintenance interventions in order to avoid false track occupancies, which means that the track is erroneously considered in an occupied state due to variations of the current levels in the track circuits. The decision support system is able to plan in advance the interventions, allocating maintenance activities to the available time-windows and to the available maintenance teams, avoiding corrective maintenance interventions that often imply service disruptions. The optimal planning is evaluated according to the detection of anomalies of track circuits and the prediction of track circuits’ status. Moreover, the planning is focused on a risk-based approach, considering the criticality of the track circuit by taking into account the consequences that its failure has on the system performance, due to the relation between system, subsystems and components.
• Use case - Operational daily planning: The business case aims to support the optimal prioritisation and allocation of interventions into possession windows in order to avoid interruptions of the daily traffic and to ensure a high utilization of the pre booked possession widows. The planning considers the cost evolution of the intervention as well as their criticality with respect to possible defects. The IAMS use case therefore optimizes operational planning, guaranteeing asset integrity with an optimal exploitation of working time.
• Use case – Earthwork asset management: the business case consists of a strategic decision support system that, using a Petri Net framework, is able to investigate how effectively different intervention strategies are able to offset the effect of asset degradation on a portfolio of earthworks assets, under budget and resource constraints. Therefore, the use case allows the selection of work volumes at the portfolio level, which are able to sustain asset performance levels at the lowest whole life cost. A dynamic model representing the behaviour of earthworks and their response to planned actions is developed. Finally, an optimisation routine to determine the best planning strategy for the infrastructure is constructed. The optimisation routine therefore compares various intervention scenarios and choose the best option.

Final results

in terms of progress beyond the state of art the the IAMS finalization will include the in field validation of monitoring solutions such as unmanned autonomous vehicles; the validation of an open standard interface for maintenance applications; the validation of analytic tools for automatic detection of anomalies and prediction of railway assets decay; the implementation of In-Lab demonstrators of the Data Analytics Architecture; the in field validation of decision support tools for long, mid- and short-term maintenance planning; the in field demonstration of maintenance tools, using technology applicable for the next generation robot platform. Finally, the decision support system applications could be extended by including other assets and new input data, but also by developing added-value functionalities. All the activities are expected to finish with technologies demonstrated/validated in a TRL6/7 environment as a continuation of the work already conducted and that will be furtherly conducted until the end in the IN2SMART project.
In fact the entire project scope is to aim towards an intelligent asset management systems (IAMS) according to ISO 55000; taking into consideration that IN2SMART is the first step only aiming to reach TRL4-5 results.
In this view some tangible progress beyond the state of art are currently in progress inside the RIMMS WPs, whilst they are clearly premature for DRIMS and IAMS WPs.
As a logic view, the continuity with IN2SMART2, currently under call for members proposal preparation, will allow the completion of the expected potential socio-economic impact.

Website & more info

More info: http://projects.shift2rail.org/s2r_ip3_n.aspx.