Predictive Maintenance

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Predictive maintenance determines the actual condition of equipment and predicts when maintenance should be performed.

This approach promises cost savings because tasks are performed only when warranted.

Contents:

  1. Objectives.
  2. Benefits.
  3. Approach.
  4. Conclusions.

Objectives

Predictive maintenance looks for anomalies, i.e., measurements that are unexpected and might indicate a problem - but are not yet so severe that they are a failure.

The challenge is to determine the condition of in-service equipment to predict when maintenance should be performed and to prevent unexpected failures.

Benefits

Predictive maintenance allows not only to manage problems that could arise in the present but also to prevent future unexpected events.

IMPROVE PLANNING

REDUCE REPAIR COSTS

REDUCE PRODUCTION LOSSES

REDUCE DOWNTIME

Approach

The way to predictive maintenance is to model equipment failures based on observations of past machine runs and failures.

Neural networks can model the correct operation of the equipment at a given condition and detect when this operation is an anomaly.

That allows to early spot potential failures of equipment and fix them before they happen.

Conclusions

Predictive maintenance makes companies save money since they will have shorter downtime and less lost production, better planning of people, and materials and reduced repair costs.

Neural Designer uses machine learning to build predictive models that represent a broad range of variables associated with the failure of equipment.

Related solutions:

Related examples:

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