Predictive maintenance determines the actual condition of the equipment and predicts when the company should perform maintenance.
This approach promises cost savings because the company only performs tasks when warranted.
Predictive maintenance looks for anomalies, i.e., unexpected measurements that 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 the company should perform maintenance and prevent unexpected failures.
Predictive maintenance allows for managing problems that could arise in the present and preventing future unexpected events.
REDUCE REPAIR COSTS
REDUCE PRODUCTION LOSSES
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 early spot potential equipment failures and fix them before they happen.
Predictive maintenance saves companies 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.