Internet-of-Things (IoT) technology is being increasingly employed by industries to move from manual, reactive maintenance to intelligent predictive monitoring that helps prevent equipment failures before they occur.

Integrated sensors, digital twins and real-time analytics can be used to provide live asset visibility, automated alerts and mobile access to detailed performance data, helping to improve uptime, reduce operating costs and enhance long-term drivetrain reliability.

SEW-EURODRIVE notes that predictive maintenance is now a core requirement for modern operations and is widely used across sectors ranging from mining and automotive to agriculture, ports, airports, and food and beverage production.

SEW-EURODRIVE business development manager: electronics Willem Strydom explains that customers are increasingly seeking smarter asset intelligence solutions that provide real-time insights into operations. He says that the company’s DriveRadar IoT suite provides an ecosystem of intelligent sensors, edge devices and cloud-based analytics offering complete operational visibility.

SEW-EURODRIVE works manager: megatronic Werner Engelbrecht notes that manual plant surveys are inadequate in today’s dynamic production environments and become quickly outdated as equipment is replaced or repaired. The DriveRadar solution by contrast is able to capture every new item added to the plant, offering a live, accurate and continuously updated asset overview.

Engelbrecht explains that real-time accuracy is essential for effective decision-making, enabling

operators to respond to the system’s insights and avoid catastrophic breakdowns. The DriveRadar system relies on SEW-EURODRIVE’s integrated drivetrain ecosystem rather than third-party add-on sensors. Strydom highlights that the company’s frequency inverters function as highly accurate, multi-function sensors. Each inverter measures time of operation, energy consumption, load and torque and detects vibrations or shocks - generating hundreds of parameters for every device.

Data collected includes temperature, ambient conditions, oil levels and ageing indicators, load variations and vibration signatures extracted directly from motor harmonics.

The information is combined to create a digital twin of each drivetrain. The digital twin uses AI-driven models to learn normal operating behaviour and any deviation from this baseline is detected immediately, enabling early identification of bearing damage, prediction of brake lining life, forecasting of oil change intervals, detection of structural faults and identification of load inefficiencies. The system can also monitor non-SEW-EURODRIVE components, making it suitable for applications such as conveyors or pick-and-place machinery.

Training in the use of the system is available on site and through the company’s Drive Academy in Johannesburg.