Predictive Maintenance: Optimizing Asset Effectiveness through Machine Learning

Here you can find out everything about our research and projects on predictive maintenance and condition monitoring.

The optimization of equipment effectiveness mainly is based on two measures:

• Minimizing downtime
• Maximizing availability

Reactive maintenance is difficult to plan due to spontaneous errors. Longer maintenance times are the result. Risks of failure are reduced by regular maintenance intervals. However, this is at the expense of the equipment's productive operating time.

Condition Monitoring and Predictive Maintenance

»Condition Monitoring« of equipment detects critical events and conditions with highwear potential. Events and faults are classified and evaluated.  Critical events or adverse operating states can be eliminated immediately by rapid reactions in order to avert cost-intensive consequential damage.Downtimes are reduced because service technicians, spare parts and logistics can be made available in a targeted manner through appropriate diagnostics.

»Predictive Maintenance« estimates risks of unwanted operating conditions and events. These predictions enable demand-oriented planning of service and maintenance activities. They are created for both, individual equipments as well as equipment parks. Ideally, predictive maintenance maximizes equipment availability and provides early information for targeted maintenance actions.