A shift from reactive maintenance to real-time, data-driven decision-making is increasing and accelerating across heavy industry and positioning predictive analytics as a critical lever for operational efficiency, says SEW-EURODRIVE mechatronics works manager Werner Engelbrecht.

He says that traditional maintenance approaches are rapidly losing relevance in modern production environments, highlighting that the energy surveys undertaken by the company often point to industry’s use of outdated and suboptimal motor and drive equipment, leading to a growing disconnect between static maintenance records and actual plant conditions.

“That gap is driving uptake of predictive maintenance platforms such as the DriveRadar Internet of Things (IoT) Suite, which provides continuous visibility of drivetrain performance across sectors including mining, automotive and food processing,” Engelbrecht says.

He notes that in the past, plant operators often relied on manual inspections and scheduled surveys to assess the health of their motor and drive assets, adding that those methods, while once standard practice, are increasingly incompatible with fast-changing industrial environments where equipment configurations evolve regularly.

Moreover, Engelbrecht says that digital systems eliminate this lag by maintaining a live asset register, with the SEW-EURODRIVE DriveRadar capturing every new item added to a plant, offering a live, accurate and continuously updated asset overview.

He says this transition reflects a broader move toward integrated asset intelligence, combining sensors, connectivity and analytics to provide real-time operational insight.

“Instead of relying on snapshots of the plant condition, engineers now have access to continuous streams of performance data,” Engelbrecht explains.

At the centre of this shift is predictive maintenance, which uses data modelling to detect early signs of equipment degradation, with the impact on reliability proving significant in this regard.

By identifying anomalies in parameters such as vibration, temperature and load, these systems enable maintenance teams to intervene before faults escalate, resulting in a reduction in unplanned downtime, improved safety performance and a more efficient allocation of resources, Engelbrecht notes.

He says that this approach also reshapes workforce priorities as it requires fewer routine inspections, allowing engineering teams to focus on optimisation and higher-value technical work rather than repetitive monitoring tasks.

Technological Focus, Industry Evolution

Engelbrecht says that a key differentiator in modern predictive maintenance systems is the integration of sensing capabilities directly into drivetrain components.  By leveraging embedded technologies and frequency inverters, detailed operational data can be generated without relying solely on external sensors.

He notes that this data feeds into digital twin models, which are virtual representations of physical assets that replicate real-world behaviour and establish baseline operating conditions.

Deviations from these baselines can be flagged as potential faults, allowing for early intervention.

Engelbrecht highlights that such systems are capable of identifying a range of issues, from bearing wear and brake degradation to broader structural anomalies, adding that they also support the optimisation of maintenance intervals and energy use, thereby contributing to overall plant efficiency.

Importantly, he says that these platforms can monitor both proprietary and third-party equipment, enabling plant-wide visibility across complex systems, such as conveyors and automated handling lines.

Engelbrecht adds that accessibility is another major advantage given that engineers can now access diagnostics and performance data remotely through mobile devices, a capability that is particularly valuable for operations in remote or infrastructure-constrained locations.

To support this digital transition, training and skills development remain critical, and programmes focused on advanced maintenance technologies are helping engineers and technicians adapt to increasingly data-centric environments.

“As industries continue to embrace Industry 4.0 principles, predictive maintenance is evolving from a specialised tool into a standard operational requirement. For plant operators, the shift signals a broader transformation. Maintenance is no longer just about fixing failures, but also about preventing them altogether through continuous, intelligent monitoring,” Engelbrecht concludes.