We love to develop easy solutions that bring our industry further
We are currently working on four development projects to bring our industry forward.
We have developed an open, efficient and simple inhouse fleet management system based on power apps. It is ECM compliant, flexible and cost efficient. Our clients are free to use and adopt it to their situation. Ideal for all clients who are looking for a solution for medium sized companies.
To enable our ambitions with real time vehicle monitoring and control, we are developing an independent onboard monitoring device which can process large amounts of real time sensor data in a cost efficient way and make that data available in a meaningful way.
We are experimenting on an augmented reality application to be projected over a smart glass or cell phone. The application identifies components and directs the user to manuals, instructions and reports. A car-based prototype has been tested and we are now working on upscaling the algorithm and embedding it in an application.
Our most demanding project is applying the possibilities of current machine learning strategies such as support vector machines, deep learning, fraud detection systems and multivariant regression to real-time monitoring the performance of the railway system so that we can detect abnormalities within the system behaviour as possible indicator of upcoming failures.
Many current monitoring systems rely on supervised learning or experts knowledge to indicate future failure. For example carbon levels in oil samples or resistance and ampere measurements in electrical circuits. But also the error messages in the vehicle system. Also more sophisticated indicators exist such as temperature development graphs in diesel engines or pressure of injector pumps. Or speed versus load versus engine rotations versus gear ratios.
We were inspired by one example of a specific version of the Br189 which very frequently came to an emergency halt in the Stuttgart area. After 1.5 years of evaluating it became clear that 1 bailiese on the track was slightly outside frequency and combined with a very tight frequency setting of this type of 189 (partly due to specific country specifications and requirements of other safety stems), it came to this emergency stop/halt.
We are experimenting with developing an unsupervised abnormally detection system that provides feedback on the real-time systems performance.
Contact us if you are interested in learning more about us.
