KU LeuvenThe Spindle Listener: Making spindle wear visible

Long story short

Unexpected downtime in industrial environments often originates in rotating machinery or linear drives, where early signs of wear remain hidden until failure occurs. To address this challenge, KU Leuven’s M-Group initiated a research project to develop predictive maintenance strategies based on measurable signals such as vibrations and motor currents.

To support this research, CTRL Engineering, together with several partners, developed a custom spindle test bench that allows researchers to capture and analyse real motion data under controlled conditions

Working with CTRL Engineering gave us a robust platform to bridge research and industrial practice.🏆
Pradeep Kundu, Professor @ KU Leuven M Group

The Challenge: Making hidden spindle wear measurable

Linear drives and spindles operate under high speeds and precision, but early wear signals are almost invisible. KU Leuven set out to explore how measurable signals such as vibrations and motor currents can be used to develop predictive models indicating when maintenance is required—before breakdowns occur. For this, they needed a test bench that could combine fast, accurate motion with synchronized data acquisition.

The solution: A fully integrated spindle test bench

CTRL Engineering developed a fully integrated spindle test bench by combining expertise in system design, motion control, and data acquisition. The backbone of the system was Beckhoff PLC and motion technology, providing deterministic control and a simple HMI.

HIWIN servo drives enabled dynamic motion, while Vansichen Linear Technology contributed to the mechanical spindle system. To capture data, CTRL Engineering integrated NI USB-4432 devices—chosen for their ease of us and external triggering capabilities. This made it possible to synchronize every movement cycle with data recording. MathWorks support packages enabled researchers to immediately process results in MATLAB and Simulink, reducing manual steps.

Results: Reliable datasets for predictive maintenance

The test bench delivers reproducible motion cycles while continuously logging motor currents and vibration signals. This enables researchers to build validated datasets, correlating subtle anomalies with spindle wear. The outcome: a reliable foundation for predictive maintenance models.

Why it matters to you? From reactive to proactive maintenance

Predictive maintenance extends machine life, reduces costs, and increases uptime.

This project proves how CTRL Engineering integrates motion control, data acquisition, and software into a complete solution—helping industry shift from reactive to proactive maintenance.