ROTEC GmbH Smart Devices and Data Analytics enable Innovative Rope Testing
ROTEC GmbH was founded in Stuttgart, Germany, in 2017. Its aim was to make an extremely well-founded body of expert knowledge of ropes and rope systems accessible to various
customer groups. As recognised experts on cable cars, ROTEC (an acronym derived from ROpe TEChnology) carry out inspections on new and renovated cable cars, as well as regular maintenance inspections on ropes and cable car systems. The company also develops a huge variety of standard and specialised cable testing systems, which are used worldwide and sold under licence.
ROTEC recently collaborated with inovex to develop software for their latest magnetic rope testing (MRT) innovation, a departure from the standard solutions of the industry.
Rope-checking sensors generate large quantities of data
In order to ensure the safety of steel cables, they are checked at regular intervals to ensure that they conform to current standards and legislation. The magneto-inductive rope testing process, which detects breaks in a rope’s wires, is the standard procedure used.
In order to carry out this type of testing, ROTEC has developed a new sensor platform that combines conventional coil measurement with echo sensors which can be read extremely quickly. While this method enables the high-resolution inspection of ropes, it also generates extremely large amounts of data, all of which must be recorded and processed in real time.
The solution – an autonomous readout device
Since the steel cables to be tested are not always easily accessible, ROTEC decided to place their new sensor platform with an embedded computer inside a compact device. The aim was to carry out the rope testing process (the recording and analysis of measurement data) via a wirelessly connected laptop or tablet.
Based on these requirements, a client-server architecture was chosen. The embedded computer handles the sensor readout as well as the processing and storage of measurement data, while a web app controls and displays the measurements. The software also supports rope testing personnel by providing semi-automatic analyses, such as evaluations of a rope’s condition according to various international standards.
Embedded computers enable automatic evaluations
In addition to its use by rope testing experts, the architecture also enables the solution to be used as a smart device for convenient system monitoring. In such cases, multiple devices are permanently installed in areas such as lift shafts. System operators can then initiate a convenient check of all ropes from a control centre with a single push of a button. Unlike the expert device, this system evaluates the sensor data completely automatically once the measurement has been completed. It then displays a clear overview of the ropes’ condition to the system operators. Detailed information on individual ropes, such as the position of the defects on a particular rope, can also be retrieved.
Technical challenges of the sensor platform
The application is structured like a classic web app and comprises a backend on the embedded computer and a browser frontend. The frontend was implemented with React/ TypeScript and D3.js, while the backend was implemented in C++ due to the limited hardware resources of the embedded computer and the sensor platform’s connection requirements.
The high resolution of the measurement data and the resulting data rate of over 700,000 sensor values of 2 bytes per second presented both the backend and the frontend with technical challenges: In the frontend, the data had to be plotted step-by-step during recording, while the image data needed to be infinitely zoomable and shiftable for analysis. In order for everything to function smoothly, the data had to be intelligently filtered and aggregated before display.
Due to the backend’s limited hardware resources – a typical measurement cannot be retained wholly in RAM – the measurements had to be continuously streamed into files without affecting either the sensor readouts or communication with the frontend. The automatic evaluation process also had to be stream-based. The HDF5 data format for storing the measurements was a tremendous help in this regard.
In order to do justice to the innovative approach and the investigative nature of the project, an exploratory process model was chosen in which the hypotheses proposed were validated through regular experiments on cable cars. This facilitated the creation of multiple revisions and prototypes based on empirical evidence, allowing the hardware to be continuously improved parallel to the software.
This was a ground-breaking project. Among other things, new algorithms for analysing measurement data had to be developed.
The fluid, real-time display of high-resolution data in the frontend and the many abstraction layers in the backend – from the hardware driver to the stream processing of the sensor data to the input validation in the web context – also presented particular challenges. From start to finish it was an exciting, exploratory, and multi-layered project, one we were able to complete together.