leanbi
08/11/2020

LeanBI boosts digitization in rolling stock maintenance in rail traffic

In a joint project between LeanBI AG and Prose AG at Zentralbahn AG, we are investigating how artificial intelligence AI and machine learning can reduce rolling stock investment and maintenance costs and compensate for the loss of knowledge by retiring operation and maintenance experts.

 

Failure statistics from recent years show three conspicuous systems that are responsible for well over half of the total downtime of rolling stock: doors, train security and traction converters.

 

The project investigates rolling stock that supplies cloud-based sensor data to an artificial intelligence system during operation, which continuously analyzes this flood of data and generates cause-and-effect synapses through machine learning in a self-learning manner.

Fig.1: Basic architecture of a knowledge-based system based on ontology and AI

 

The reduction of rolling stock downtime is achieved by transforming static maintenance processes into dynamic operations.

 

The ontology is used in machine learning to align the language structuring and later processing of the data. It describes the relationship between the individual knowledge stocks and thus creates the prerequisite for machine learning to be applied. For the use case of rail vehicle maintenance, the essential component of ontology development is the cause-effect modeling between the error descriptions and categorization and their elimination or reporting. Only in this way can they be processed by machine and logical conclusions and optimizations derived from them.

 

The scalability of the achieved results and situation-based maintenance are the main focus of this project. The transformation of the maintenance organization into a knowledge-based, agile process structure will then be worked out.

 

Based on a joint proof of concept at Zentralbahn AG, the present article has been developed, which shows the exciting development possibilities of Artificial Intelligence and Machine Learning.

 

Here the link directly to the magazine ETR (subscription required):

https://eurailpress-archiv.de/SingleView.aspx?show=1941606

 

Here is the link to the ad in LinkedIn, where the article can be read directly.

LinkedIn

 

Please read this really exciting article that shows the fundamental paradigm shift.

 

 

 

Marc Tesch
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