Digital Twin - Boom Fahrzeug
CEO: Bora Man
Bora Man

Digital Twin - The rail vehicle as a virtual asset in fleet management

Post from 30. June 2020

Shorter lead times and improved efficiency through continuous real-time data analysis. Benefit from a holistic view of the vehicle: 360° on 365 days.

New horizons through digitalization

The entry of IoT into the world of rail vehicle digitalization has opened up completely new horizons for data and information management of rail vehicles. Comprehensive technological developments in the fields of telemetry, sensor technology and telecommunications enable the timely and comprehensive acquisition, transmission, storage and processing of vehicle-specific status data and thus the mapping of the real object to a virtual asset. These data comprise on the one hand classical technical information and measured values of the vehicle and its components, which can be used as a basis for maintenance development, and on the other hand data and information from the system environment.

360°/365 days view on the rail vehicle

It is now possible and desirable to logically and chronologically link all context-related information of the rail vehicle (asset). For this purpose it is necessary to generate an integral, encapsulated and uniform view of the system “railway vehicle”, as the sum of its components in the subsystem “rolling stock” in the sense of the TSI, as well as in interaction with its environment (interoperability), the entity “asset”.

These data domains refer on the one hand to the respective subsystems of the TSI, and on the other hand to logistic and commercial sources of information about the lifetime of the rail vehicle.

Softwarelösungen speziell für die Bahnindustrie

The causal link between data domains and information categories enables a 360° view, 365 days a year. Therefore, a comprehensive view of the overall condition of the rail vehicle is generated (Digital Twin), which can be traced over the timeline in terms of use and condition, or the probable future condition of the rail vehicle can be determined, or an optimized deployment and schedule can be proposed. Thus, this is the basis for “predictive asset management” in the sense of further development and optimization of maintenance strategies, reliability of the rail vehicles, optimization of deployment and productivity, commercial exploitation of the available and enriched information, basis for research and development of further integrated technologies, as well as the generation of new business models based on the available data structures.

 

Optimization through artificial intelligence

The resulting data-lake also forms the basis for the implementation of applications with artificial intelligence for the optimization and prediction of maintenance recommendations, production, deployment and circulation optimization of individual assets, the fleet and consequently the entire fleet management. These recommendations and predictions, generated by deep learning from empirical data and rule-based algorithms, serve to support and automate the management of individual assets and the fleet. Thus, for example, typical wear and maintenance curves can be developed by means of these learning methods on the basis of the empirical data, which can then be applied to components on the basis of the currently available information on the condition of components and thus future conditions, failures, maintenance requirements or operational performances can be predicted or suggested.

The need for maintenance is basically specified here via the underlying maintenance strategies according to the vehicle manufacturer or maintenance developer (ECM2). The application of component-typical wear curves to the current wear curves of the respective installed components results in proposals for the remaining mileage and thus for the necessity of servicing or maintaining the component. This, laid over the entire parts system of the vehicle, provides the possibility for condition-based modularization of maintenance intervals.

Requirements for Enterprise Asset Management Software (EAM)

In order to be able to map a digital twin of a asset, it is necessary to apply a software that guarantees the primary focus on the entity – asset – (rail vehicle). The asset is the mainstay of the “Digital Twin”. Thus, the starting point is a comprehensive asset management system that masters all data domains and information categories. In order to ensure the continuity and integrity of data and processes, the sphere of maintenance and repair (ECM2-ECM4) must also be covered.

A connection to the production systems, depending on the primary purpose of the rail vehicle (passenger transport, freight transport, local transport, long-distance transport, etc.) represents an obligatory framework condition for the holistic representation of the digital twin.

Technologically, the software must be capable of delivering a balanced set of standard processes and interfaces “out of the box” and also offer the possibility of adaptation (customizing) or individual adaptation (total customizing). This is achieved in the BOOM RAIL SOLUTIONS by sharply demarcating and assigning processes, competencies and responsibilities to the individual modules, thus ensuring high data integrity for the continuous mapping, tracking and management of the digital twin – rail vehicle.

The illustration shows the implementation status as well as the roadmap to the current state of the art of the development of “Digital Twin – Rail Vehicles” of Boom Software AG.

According to this, digitalization of rail vehicles and in the railway industry in general is a far and wide topic for the future and an absolute necessity in order to be able to meet the security and performance requirements of ever-increasing performance and capacity demands.

Implementation status and roadmap of the Digital Twin
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