Romonto

The integration of diagnostic functions into a (new) rail vehicle is key to linking operating data and maintenance in an effective manner. The first step is to define smart maintenance requirements for the vehicle. This includes information on which systems are to be taken into account, as well as the system status to be measured, including its evaluation thresholds. In a second step, a requirements and system specification of the diagnostic system is created. This takes into account the functional scope, the interfaces and fulfilment criteria during validation to ensure high reliability and accuracy of the diagnostic data.

Enotrac offers two basic options for recording diagnostic data:

• Embedding software functions into the vehicle control system (e.g. in refit projects)
• Use of separate hardware which is connected to the data networks and manages diagnostic data on the vehicle. The device takes over the data transfer to the wayside database. The vehicle control system can thus be relieved of the diagnostic functions or centralisation can be implemented without feedback effects.

The transmission follows the specifications of IEC 61375-2-6, taking into account the customer’s IT environment.

The specified requirements are validated as part of the type testing of vehicles. Enotrac provides support in analysing the test results and feeding the findings back into the optimisation of the diagnostic system. This ensures a reliable database during operational testing and allows weaknesses in the implementation to be recognised and eliminated at an early stage.

With the collected data, the characteristics of the measured variables such as sensitivity, scatter and their influencing variables can be quickly “learnt” and evaluated.

Enotrac’s Romonto offers a homogeneous yet flexible modelling of the vehicles to which the incoming data is linked. In addition to mobile data sources, infrastructure sensors can also be connected. Functions and performance can be adapted to the customer’s needs in a most flexible way.

A crucial component for the success and benefits of a centralised diagnostic system is the processing of the stored data. Thanks to the clear presentation of the relevant events and fleet statuses, anomalies can be quickly recognised and countermeasures initiated.

Enotrac offers web-based tools for accessing the data, which can be largely adapted to the customer’s needs and jointly optimised. All types of end devices are supported. Parallel to visualisation, the data can be automatically forwarded to other processing tools via interfaces.

The inclusion of a large amount of high-quality data, which is normally generated in a diagnostic system, is the basis for utilising the benefits of predictive maintenance. The preceding specification and validation steps ensure that necessary and correct data is available.

In close cooperation with experienced partners from the field of big data analysis and experts from the field of rolling stock maintenance, Enotrac is able to carry out in-depth analyses of the stored data and to interpret it in the railway environment. This allows maintenance to be planned proactively, for example, if the failure of a component is foreseeable, or weaknesses in the system design to be eliminated in a targeted manner.
This can be demonstrated using the example of door failures. Automated detection of door failures allows an early response to be made before a manual notification is made in the system.

Enotrac can link the knowledge gained from the analyses with the operator’s existing maintenance management tools , allowing the information to flow back directly.

This includes, for example

• Automated notification of maintenance about defects that have occurred instead of manual notifications
• Prediction of the remaining service life of a subsystem together with a maintenance recommendation
• Automated creation of maintenance orders, e.g. for material scheduling.