Acoustic io t rail monitoring.pptx

Editor's Notes

1. Thank you for inviting us!! It is a great pleasure to be here. My name is FA and this is DM. We represent IBM and Tyrens – who in joint partnership have developed a first of a kind IoT acoustic solution for monitoring of rail tracks…
2. We can deliver the data that will enable track owners to move to a predictive maintenance model! We can also support cities to reduce the noise levels from the metro and railway with the possibility to free up land for construction closer to tracks..
3. It is not easy to get an exact number on this – but based on available statistics we spend more than 25 bn Euro every year on rail track maintenance. Just to put that number in perspective. It is the equivalent of the construction cost, excl land, to build more than 800.000 80m3 apartments per year. Or providing education for 3 million students per year (OECD average) You get the point – it is a lot of money
4. Today track owners cover their assets with more or less advanced and CAPEX intensive equipment on an infrequent basis. These random data sets are not sufficient to detect the cause and effect of the wear and tear on the tracks.. Scheduled maintenance Capex Intensive analytics equipment Infrequent Inventory of tracks Manual inspections Tendering based on unknown track conditions
5. This spend of 25bn Euro – is done through scheduled maintenance. As we all know, this is not the optimal way of working with maintenance. We have not come across any rail track maintenance company that has gone to a predictive model – so, to state the level of saving is difficult to assess – but looking at other industries where this is done typical savings come start at 15%.
6. So, we have developed Quiet Track Monitoring System.. An acoustic IoT Track monitoring solution, capturing real time analytics and provided as a service….
7. History of the project started out with an analysis of noise….
8. We now use a set of sensors on board the trains to detect these 7 use cases…
9. The solution uses acoustics and other sensors to monitor and detect anomalies and change over time. The data is linked to the IBM Cloud where it is mapped to a linear model and captures alarms. In short: an acoustic IoT solution delivered as a service.
10. The solution uses acoustics and other sensors to monitor and detect anomalies and change over time. The data is linked to the IBM Cloud where it is mapped to a linear model and captures alarms. In short: an acoustic IoT solution delivered as a service.
11. We use the existing BIM or asset models. On top of that we detect, map and store the operational data The data is captured in real time and is mapped and stored linked to the linear model. The combination of the data model of the physical asset combined with the operational data creates a digital Twin of the tracks.
12. A digital twin enables you to start doing some real analytics on cause and effect and will enable you to move towards a predictive maintenance model.
13. We are now live with the solution at the underground in Stockholm after wining a public tender… We have equipped 7 trains with our solution and can cover all tracks in real time… The data
14. The data collected on the 7 trains are sent up to the IBM Cloud where we map the data to the linear model, we set the alarm thresholds and detect anomalies. The alarms are sent to the client through API’s. Once we have more data we can start activating more advanced analytics on the data set and move to predictive maintenance.. We are also able to export the operational data to any asset management system, such as Maximo to create work orders.
15. In the user interface you can drill down to each individual section of the track to analyze specific alarms per use case and see the history of that data set.
16. Here you can see the 7 use cases in the UI so the user can go in and filter on exactly what it is you want to see..
17. It is built in a responsive web format to accommodate any device type used… The next step is now to deploy this to a national rail network with trains operating at higher speeds and build in Cognitive Analytics to the platform to detect the cause and effect relationship between the 7 use cases and thereby enabling real bottom line savings through predictive maintenance and providing real feedback to Infrastructure Designers and engineers. ..
18. We can deliver the data that will enable track owners to move to a predictive maintenance model! We can also support cities to reduce the noise levels from the metro and railway with the possibility to free up land for construction closer to tracks..
19. So if you also want to take your rail maintenance to the next level – please do not hesitate to contact us!