Gian-Piero Pavirani
Rete Ferroviaria Italiana (RFI)
Speaking on 23 November during the Rail Infrastructure Measuring and Monitoring sessions

pavirani_gianpieroGian-Piero Pavirani
Rete Ferroviaria Italiana (RFI)


Extracting information from data for railway infrastructure maintenance decision support

The questions covered during this session include:

  • How condition data can be used for asset management?
  • Why a Decision Support System integrating asset, condition and activity data is important?
  • What information can be extracted from data for maintenance planning and control?

Abstract

The increase of the railway infrastructure availability, the improvement of safety features and the reduction of the maintenance budgets impose on any railway infrastructure manager a better control and planning of Maintenance and Renewal works. This requirement can be satisfied with the adoption of a Decision Support System (DSS) and involves different actors such as Diagnostic Operators, Maintenance Planners and Work Centers as well as different data such as asset, activity and condition data managed within the maintenance process.

Nowadays measuring vehicles/trains are capable to collect a wide range of diagnostic data. Within the maintenance process, it is necessary to turn data into information, making best use of the data available, whilst eliminating the risk that processing an excess of data may waste time or lead to vital data not being used at all. Condition Data is important not only to understand current asset conditions but also failure causes and condition evolution over time. For example measurements and defects (based on predefined thresholds) can be further processed with proper business rules and models to identify:

  • the right work timing and priority
  • the actions required for optimization of resources

Rete Ferroviaria Italiana (RFI) – Italian Railway Infrastructure Manager – introduced a DSS, named InfraManager to further process available condition data to support maintenance and renewal decisions. InfraManager integrates several data and business rules to ensure users accessing to the right information for maintenance decisions.

The objective of the InfraManager is to enable more efficient and effective maintenance of railway infrastructure. The main output includes:

  • A linear graphical visualization of data
  • Condition Trending based on a set of deterioration models
  • Configuration of business rules for ballast, rail, overhead line and switches
  • The list of proposed maintenance works to be planned
  • A list of renewal priority/scores to be used to assess what renewals must be considered

For RFI with more than 25.000 km of track spread over 15 regions the design and implementation of such a DSS has not been an easy task. In fact, the system delivery strategy has included Proof-of-Concept and Pilot stages before the complete rollout over multiple asset types and the entire network. Moreover, specific aspects received particular attention such as: data availability and quality checking, data integration with information systems in place, coverage of usability requirements in line with final user expectations and others.

Main improvements introduced thanks to InfraManager include:

  • Visualization of the required information in a single and common user interface without the need to adopt custom tools
  • Enhanced understanding of defect occurrence and causes
  • Clustering of defects for better targeting maintenance works
  • Monitoring quality deterioration and improvements in time with predictive rules/models for better maintenance planning and control

All the above improvements lead towards significantly better controlling of the condition and behavior of railway infrastructure assets at any given point in time, thus increasing their reliability and enhancing traffic safety at a minimum life cycle cost.

View the topics of discussion and the speakers for each individual theme:

We look forward to welcoming you in Naples between 22 – 24 November for the Intelligent Rail Summit. 

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