Predictive Maintenance in practice

Every machine failure or downtime due to maintenance results in real financial losses—lost time that neither generates profits nor satisfies the customer. That’s why we focus on assessing machine condition, predicting the likelihood of component failures, and identifying minor damages before they become critical. By doing so, we prevent unplanned downtime and extend the service life of machines. This not only leads to cost savings in production but also ensures the high product quality that comes from properly functioning equipment.

Key monitoring parameters

Motor speed during operation
Noise levels
Temperature fluctuations
Early signs of abnormal vibrations or mechanical imbalance

We then conduct a detailed analysis of these data points, allowing us to determine if a specific component needs replacement. Properly planned component replacements are essential for efficient maintenance operations, aligning with varied production volumes. This enables operators to replace worn parts at an optimal time without interrupting ongoing production, positively impacting OEE (Overall Equipment Effectiveness) and MTBF (Mean Time Between Failures).

Our predictive maintenance tools

Condition monitoring
Non-intrusive testing
Visual inspection
Automated condition monitoring

Condition monitoring & diagnostics

Collected machine data provides real-time insights into its operational status.
These insights form the basis for service decisions, offering added value by presenting machine condition and diagnostics in a clear, user-friendly report.
Reports are customized for specific user groups, whether in operations, finance, maintenance, or other departments.

This enables us to use the data we collect to make maintenance decisions. We present this information in clear and user-friendly reports that can be accessed by different departments in the organization, such as operations, finance, or service.

Key success factors for predictive maintenance

The success of condition monitoring and diagnostics depends on clear goals and well-defined information recipients. What exactly should the collected data indicate? Who should have access? Based on our experience working with various industries, we have developed four fundamental principles for effectively implementing predictive maintenance in a plant:

Select production assets wisely—focus on critical machines where failures could immediately disrupt production.
Choose the right partners—especially for advanced predictive maintenance, requiring documented expertise and investment in knowledge and development.
Allow time for refining models—predictive maintenance is a continuous improvement process.
Prioritize employee involvement—successful implementation requires both an accurate model and a cultural shift in workplace operations.

By following these principles, companies can achieve greater efficiency, avoid unplanned downtimes, and maintain high product quality.

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