TRANSPORT-Transport 2025

Vi har fullført designet vårt med et industrielt IOT-modem, vi har også lagt til en IMU til systemet vårt. I det endelige designet lagrer vi alle rådata hvert 10. minutt på SDCard. Datalagringskapasiteten vår er økt fra noen få megabyte som var opprinnelig plan ved å bruke NOR Flash-minnebrikker til 64 GB ved å bruke et høyhastighets SDCard. Vi lagrer også GPS-dataene som vi får fra modemet. Så vi merker hver fil med opptak med tid og sted for anskaffelsen, og senere dataforskere kan analysere data basert på plasseringen av toget og hastigheten. Vi lagrer også akselerasjonen og helningen til toget i 3D i overskriften på hver fil. Dette gir ekspertene mulighet til å normalisere sin vibrasjonsanalyse med de mekaniske belastningene som er påført boggier under hver anskaffelse. Vi har lagt til muligheten til systemet vårt for å konfigurere inngangskortene med forskjellige typer sensorer. Selv om akselerometrene vi har brukt er industrielle sensorer med IEPE-standard, men det er mulig at dataanalyseteamet i noen tilfeller trenger en annen type sensorer med spesifikt grensesnitt. Så vi har vurdert universelt spenningsgrensesnitt og også temperatursensorer i designen vår. I fastvaren bestemte vi oss til slutt for å lage en oppstartslaster for å starte fastvaren fra SDCard. Så hvis vedlikeholdsteamet ønsker å samle inn data for lengre tidsperioder på ett tog selv med kortere prøveperioder, kan de enkelt gjøre det nå ved å lage to kopier av SDCards og erstatte dem når systemet er i nærheten av å fylle gjeldende SDCard. Vi har testet denne mekanismen i mer enn to uker, og den fungerte bra. Så det er mye mer enn nok for togturer i Norge som tar få dager. Vi har også utviklet aconsule for feilsøking av systemet og kommunikasjon med systemet mens det kjører programmet. Så vedlikeholdsteamet kan være sikre på ytelsen til systemet mens det logger dataene og er montert på et tog.

Leveraging the power of technology, we have successfully developed a system to relentlessly monitor the health condition of train bogies while they are operational on the tracks. This innovative approach revolutionizes our ability to maintain the safety and efficiency of our railway systems. The beauty of this system lies in its potential for precision. The maintenance team, traditionally reliant on predetermined schedules based on estimated running hours, can now utilize a more accurate forecast for maintenance activities. This is achieved by focusing on the conditions of each specific bogie, as revealed by the ongoing monitoring technology. The ability to customize maintenance schedules for individual bogies dramatically enhances the accuracy and effectiveness of our maintenance operations. A significant advantage of this development is its potential for reducing unnecessary overhauls. Traditionally, these overhauls are scheduled based on a one-size-fits-all estimation, often leading to wasteful and environmentally detrimental activities. By tailoring maintenance to the needs of each bogie, we can greatly minimize these unnecessary overhauls, resulting in substantial cost savings and a reduced environmental footprint. Moreover, adopting condition monitoring and predictive maintenance strategies adds another layer of safety to our systems. By continuously evaluating the health of the bogies, our technology can immediately signal the need for an overhaul at the earliest signs of degradation. This early warning system ensures that necessary maintenance is never delayed, thereby significantly increasing the safety of our trains. In essence, our trains are safer, more efficient, and more environmentally friendly as they traverse the tracks with our technology monitoring their every move. We have effectively transformed the traditional maintenance approach into a more precise, cost-effective, and eco-friendly one. Furthermore, by ensuring a higher level of safety, we boost passenger confidence in our railway systems, contributing to a more reliable and sustainable transportation infrastructure for the future.

The railway sector is one of the foundations of the country's mobility. Only in Europe, rail represents 45% of public transport, accounts for 26.9 billion passenger trips each and is used to carry 18.3% of the freight goods inland. One of the main costs drivers in this sector is the maintenance of the rolling stock, which is estimated to average between 24 and 34 NOK per unit per kilometer travelled. Achieving even a small reduction in these costs can translate into substantial savings, and have a crucial impact on the overall sector's performance. Traditionally, rolling stock preventive maintenance has been based on activities that are carried out on very conservative intervals. For that reason, many activities are performed more frequently than truly required. Resources are wasted on redundant check-ups, unnecessary replacement parts and the idle time during which the rolling stock is taken out of service. By properly monitoring the real-time condition of the rolling stock, however, it is often possible to predict the need for maintenance. With this kind of information, maintenance can be carried out only when strictly necessary. This new paradigm, called condition-based maintenance or CBM, poses multiple challenges: processing large amounts of data from multiple sources, building accurate models for failure prediction and re-designing the way maintenance providers carry out their operations. The main goal of this project is to lay the technical groundwork for the transition of the Norwegian railway sector to CBM. This will result in more efficient operations and lower maintenance costs. Mantena will become more competitive in the European railway market, and its customers will be able to provide a more frequent and reliable service to its passengers.