Step by step towards a better quality of life

Greater efficiency, fewer failures – with this goal in mind, Stöber is consistently developing its predictive maintenance solutions. To this end, the drive specialists have defined several expansion stages: They continuously monitor the drive train and derive maintenance recommendations based on the analysis of relevant process and machine data. The possibilities range from analytical computational models to AI-supported methods. Stöber is thus pursuing a strategy of an integrated solution. This solution requires no external sensors or accessories and is accessible for a wide range of control systems.

"How can we use predictive maintenance to successfully transition from mere condition monitoring to real condition-based maintenance for the drive train?" asked Tim Lang, Head of System & Test at Stöber, and his team. Because to achieve higher availability, reduced maintenance effort, and longer lifecycles, predictability is becoming increasingly important for users: "How likely is it that the gear motor will fail soon?" or "When is the ideal time to service or replace it?" To answer these questions, Stöber is pursuing a three-stage development plan, the second stage of which is currently being implemented.

First stage: model-based analysis

In the first expansion stage, the user received a predictive maintenance solution that monitors the gear motor of a drive system. Its service life is calculated using an analytical model and displayed in the drive controller software by the so-called service life indicator – a value between 0 and 100 percent. Above a threshold of 90 percent, the software recommends replacing the gear motor and also makes this information available to a control system as a readable parameter. This efficient solution for predictive maintenance requires no external sensors or additional wiring. Second stage: active measurement

“In the second stage, the calculation model is supplemented with active measurement,” explains Tim Lang. To this end, Stöber integrates an acceleration sensor into its system, which consists of a gearbox, motor, cables, and drive controller. External power or voltage sources are not required. This allows for targeted monitoring of bearing seats, gear teeth, and other drive components. “Through frequency analysis, we can draw conclusions about impending damage based on the spectrum,” explains Tim Lang. “We are currently still in the prototype phase.” Stöber is working closely with DR. JOHANNES HEIDENHAIN GmbH to jointly develop a gear motor with an integrated acceleration sensor.

The goal in mind: a smarter powertrain

“Among other things, we have improved the analytical model and expanded the database to over 80,000 combinations of gearboxes and motors,” says Tim Lang. With the new LoadMatrixAnalyzer, which will be available in the coming months, customers can compare load matrices even more easily, generate standardized reports, and create individual analyses upon request. Users benefit from easier operation and improved visualization. In the future, Stöber will also provide function blocks and example programs that allow the acquired data – especially the load matrices – to be read out via EtherCAT or Profinet.

For the LoadMatrixAnalyzer, the load matrices form a solid data basis for recording real load situations. This opens up a wide range of applications for condition-based maintenance – from the detection of design and assembly influences to meaningful long-term analyses. Tim Lang: “For each analysis of the load matrices, users assign a project name. This allows individual matrices to be uniquely named, described, and provided with all relevant information for the analysis report. The LoadMatrixAnalyzer itself offers several key functions. These include the visualization of load cases. Here, torque and rotational speed at the gearbox output are displayed over time in a 3D diagram – making load situations visible at a glance.”

Furthermore, the standard limits for the motor, gearbox, and drive controller can be displayed individually or in combination. This immediately shows whether certain load ranges are within the permissible values ​​or whether deviations occur that need to be examined more closely and evaluated individually.

Finally, the results can be documented directly in the analyzer. It records whether, for example, it is a non-critical torque load or whether potential risks exist for individual components.

The LoadMatrixAnalyzer also allows up to four load matrices to be compared with each other.

This clearly shows how speed and torque have changed over a specific period. "This shows us, for example, whether a gearbox has already been broken in or whether there are indications of stresses and defects," explains Stöber expert Lang. The analyses can then be exported as a standardized report in PDF format.

Tim Lang: "For me, this tool is more than just an auxiliary program – it's a standalone software application. It's updateable, and we are continuously developing it further." The next steps: Stöber will expand its predictive maintenance solutions in the future using AI. The goal is a smart powertrain that can both recognize its own condition and provide relevant field data in real time.