Data-based models for predictive maintenance
Calculation models for the predictive planning and optimization of maintenance measures
Illustration of data-based models Picture: Joanneum Research
Unexpected faults, failures and unplanned downtime lead to a lack of efficiency in systems, machines and components. This can also affect product quality. On the other hand, excessive costs are resulting, if maintenance activities are carried out too frequently or components are replaced too often. Determining optimal maintenance intervals is a complex task that requires not only condition monitoring using suitable sensors, but also expert analysis of the data obtained and the development of reliable predictive models.
What are the benefits of predictive maintenance (PdM)?
PdM provides the basis for customized maintenance, the reduction of downtimes and the avoidance of unplanned breakdowns. Precise maintenance measures reduce wear and tear as well as early failures, thereby extending the service life of systems and components. As maintenance work only takes place when it is needed, personnel and material resources are used more efficiently.
Our offer:
- Analysis and assessment of the suitability of existing data for PdM
- Development of predictive data models for wear, degradation or service life
- Development of optimal maintenance strategies, service intervals or times for component replacement
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