Predictive Maintenance

Condition Monitoring and Maintenance 4.0 in Practice: Challenges and Experiences

Maintenance processes are evolving rapidly, with the implementation of condition monitoring playing a crucial role in the shift from reactive to predictive maintenance. Thanks to the opportunities provided by the Fourth Industrial Revolution, organizations can now leverage advanced technologies more than ever to make maintenance more efficient, reliable, and sustainable. However, this evolution also raises questions: How do you choose the right approach amid a sea of technological solutions? Which methods and tools actually deliver the best results in practice? And how does the human remain an essential link in these increasingly automated processes?

The Predictive Maintenance Learning Network offers a unique opportunity to explore these questions together with peers. Here, we not only share knowledge and practical experiences, but also discuss the challenges we encounter. Perhaps another participant has already found a solution to your specific problem, or you might learn why a particular challenge does not (yet) arise elsewhere. This platform is all about collaboration, mutual learning, and developing future-proof maintenance strategies.

The themes, dates, and locations of the meetings are determined in consultation with the participants, ensuring that the program aligns optimally with the needs of the group. We strive to create an inspiring environment centered on openness and knowledge sharing.

1. Welcome
   Introduction of the participants
   What does Predictive Maintenance look like at Unilin?
   What experiences have there been?
   What challenges exist?
2. Roundtable discussion:
   What experiences with Predictive Maintenance do each of the participants have?
   What challenges with Predictive Maintenance exist?
   Where do we apply online vs. offline condition monitoring?
3. Further arrangements with the participants
   Dates & locations
   Give & Take matrix

The next sessions will be determined in consultation with the participants.

Possible topics include:

• Choosing between online and offline condition monitoring.
• Determining the right location and physical parameters for condition monitoring.
• Tools for monitoring and visualizing conditions.
• Using AI to detect anomalies and integrating this into existing processes.
• Different ways to trigger actions, such as via email, SMS, or apps.
• Integration of condition monitoring into EAM systems like SAP and Ultimo.
• Device management and security.
• The importance of feedback and adjustment after replacement.
• The use of generative AI to support technicians.
• Discussions on Maintenance 4.0 versus 5.0.
• When condition monitoring is synonymous with predictive maintenance and when it is not.
• Applying predictive maintenance within a failure mode-based maintenance strategy.
• Optimization of maintenance intervals and choosing the right technology.
• Setting up monitoring, dashboards, KPIs, and alarms.
• Visualization, ownership, and defining responsibilities in notifications.

This Learning Network is open to companies that already apply predictive maintenance and have gained sufficient experience. Please note: only asset owners are eligible. There are a maximum of 12 places available.