Remote Brake-System Telemetry for a Mining Excavator with Jan Mikyska and HEXIO

Our partner HEXIO offers a software IoT platform that makes industrial digitalization easier. Together we delivered a project bringing telemetry to the Svendborg Brakes brake system of a surface-mining excavator.

The customer, Jan Mikyska, is the exclusive supplier and service organization for the Danish company Svendborg Brakes in the Czech Republic and Slovakia.

The Challenge

Jan Mikyska wanted remote access to operating information from the hydraulic brake system and an easier way to maintain it. The requirements were clear: an internet connection from locations without a GSM signal, fast and simple integration with the HEXIO platform, and battery operation.

The Solution

For a project in an industrial environment beyond GSM coverage, the CHESTER IoT Hub connected via Vodafone’s NB-IoT network was the obvious choice. CHESTER supports telemetry through many interfaces; here we used a configuration for four digital/analog inputs. The device has an integrated temperature sensor, an accelerometer for shock/tilt detection, and an external magnetic sensor for detecting when the switchboard cabinet door is opened.

HEXIO accesses the data through HARDWARIO Cloud - via the web portal, callbacks, and REST API. The data is then available to technicians in HEXIO’s web and mobile applications. As a result, service technicians can track the state of the brake system and perform online diagnostics of reported problems without having to travel to the site.

Benefits of Our Solution

• CHESTER connected the brake system to the internet with no need for custom development, and runs for several months on batteries
• Data is sent even from locations with no GSM signal
• The collected data supports remote monitoring, diagnostics, and historical analysis

Results

Long-term data storage lets technicians review historical metrics and analyze changes in the machine’s behavior. Working with the brake manufacturer, HEXIO also created algorithms for automatically detecting the most common faults, such as internal or external oil leaks. In the future, the collected data combined with machine learning will enable automated predictive maintenance for the company.