Lean and Smart Sound-Monitoring for Dutch ProRail

Harnessing the power of the Internet of Things movement (IoT), we built our own system for collecting data from the physical environment. From data acquisition, data transmission and storage to data analysis the system’s processing chain consists of flexible building blocks. We recently applied this Lean and Smart Sensor Network (LSSN) to detect rail wheel squeal.

We are helping Dutch ProRail to assess the effect of different squeal reducing lubrication systems. In order to tell whether or not rail wheel squeal occurred at a specific location, we were asked to carry out sound measurements. On the given site, there were just a few trainpassages per day to be expected. Measuring 'by hand' would not have given us enough data to draw conclusions from.

With our LSSN-framework it was possible to measure sound and weather 24/7 for two weeks, without having personnel on location. The LSSN framework shines when it comes to flexibility. Here the monitoring setup combined class 1 audio measuring equipment with commodity hardware for connectivity. A surveillance camera helped to trace the exact times of train passages and the types of bypassing train material. A small weather station measured humidity and temperature.

The monitoring campaign yielded two weeks worth of third octave spectra on our LSSN data storage. However, for us the train never stops at “here’s the data” - no matter how flexible, lean and smart it has been collected. We are all about providing insight into what the data means: analysing the measurements enabled us to report in detail to which extent squealing was still an issue on the given location and thus assess the efficiency of the different lubrication systems.