Following my presentation on digitisation of freight rail, Katharina presented our work together with Nowe to come up with a model of friction enhancer behaviour and effects. The work shows her novel approach to strip theory that can calculate the adhesion area considering friction enhancers very efficiently.

The work now needs to be carried further calibrating and validating the model.

Another year passed. The second annual brake friction conference, once again in Derby and I have the opportunity to present a part of my work on automation of (legacy) brake systems. This may help to get automated and autonomous trains on track soon.

In the picture, you see a Cascade Classifier trained to detect brake equipment on the wagon side. As you can guess, plenty of data is required to train such AIs – which are not collected in the railway sector currently. For all those interested in the slides, download them here.


Maybe this is due to being invited after another presentation at SmartRail, for the valued reader of my blog the topic is not totally new… However, for all of you that want to see the slides (again): they can be downloaded here.

BTW: the paper won a best young scientist award at PHM Asia Pacific this summer, so why not try it? The full paper is Creative Commons Licensed and can be found on ResearchGate.

…hat Kenneth-Bryan Hytrek eingereicht und heute hervorragend verteidigt. In seinem umfangreichen Werk geht es ausgerechnet um die Entwicklung eine Strategieprozesses für kleine Unternehmen. Der Prozess ist viel schlanker, als ist der Umfang der Arbeit vermuten lässt und führt zu äußerst interessanten Resultaten, wie er in einem Anwendungsbeispiel demonstriert hat.


Kenneth ist Mitgründer des Unternehmens Leanovate und konnte die Ergebnisse im Unternehmen bereits einsetzen. Herzlichen Glückwunsch!

After two days of Conference and Deep Learning Workshop connecting with a surprisingly high number of railway professionals working on PHM topics, it is time to face their questions and discuss current issues during the Rail PHM Panel Session.

Parham Shahidi of PARC kindly invited

  • Wan-Jui Lee (NS/Dutch Railways),
  • Brad Hopkins (Bluvision),
  • Milad Hosseinipour (Amtrak),
  • David Siegel (Predictronics)
  • and me

to join hin in the panel session.

Find my introductory slides here.

Das Semesterende hat mir heute drei (!) weitere Kolloquien beschert:

  • Jennifer Hoffmann mit einer Arbeit zum Auswuchten von Rotoren bei Hochvakuum-Pumpen
  • Benjamin Lieck hat menschliche Fehler beim Rangieren unter anderem mittels SHERPA und HFACS-RR untersucht 
  • Tobias Wall hat den Prototypen der Bremse 4.0 ausgelegt und die Anforderungen an die Komponenten ermittelt 

Herzlichen Glückwunsch Euch!

Ok, zugegeben, mit Pflügen hat er sich nicht beschäftigt – aber immerhin mit landwirtschaftlichen Maschinen und dort insbesondere mit der Auslegung einer zentralen Beschickungsanlage für Einzelkornsämschinen. Aha? Eine Einzelkornsämaschine sorgt dafür, dass im z.B. Maisfeld die Pflanzen schön regelmäßig stehen und jede den Platz für ihr Wachstum bekommt. Nicht unbedingt Rocket Science, aber doch weit mehr sophisticated, als man gemeinhin auf dem Feld vermuten würde.


Wir sehen Johannes zum Masterstudium im Herbst wieder, bis dahin viel Spaß mit dem Bachelor!

Towards the end of our trip through Asia, Parham and I had the great opportunity to visit a Korail depot for Heavy Maintenance of KTX high speed trains. Very impressive work was going on there, the lifecycle behaviour of the components differs quite a bit from Europe.


In the afternoon we gave a talk on PHM for railways form both US and European perspective to Scientists and Students at the Korean Aerospace University following an invitation from Professor Choi. 

Today I had the honor to present Parhams, Manfreds and my paper on how to apply Condition Based Maintenance in an economic fashion. Especially in our highly competitive railway environment, it is vital to think of the business case from the very beginning – just as we did in the concept of Wagon 4.0.

The current developments in Big Data and Machine Learning will provide exciting opportunities to improve the quality of service, the safety and the performance in freight rail – and perhaps the easiest way to abolish freight rail is not to make full use of these!

The slides and the paper are available for download – I look forward for your comments.