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Predictive maintenance using advanced analytics


By Pablo Martin, Artelnics.

Predictive maintenance looks for anomalies, i.e., measurements that are unexpected and therefore might indicate a problem - but are not yet so severe that they are a failure. This approach makes companies save money, since they will have shorter downtime and less lost production, better planning of people and materials and reduced repair costs.

Advanced analytics

Advanced analytics, and specifically the association models using neural networks, learns about the correct operation of the equipment at a given conditions and can detect when this operation is anomaly. In this case, an analyst can investigate the cause and decide on a course of action together with the operations staff. Industry 4.0 presents the best framework for the use neural networks due to the growth in the information that we have about the functioning of any industry.

Exclamation

ANALYZE THE PAST FAILURES
Study in depth which are the areas that present a greater percentage of failure.
Analyze
FIND THE REASONS OF THOSE FAILURES
Analyze which external or internal factors are more related with those failures.
Build
PREDICT FUTURE FAILURES
Use machine learning to predict which area is going to fail based on previous experiences.
Benefits
REDUCE FUTURE FAILURES DOING PREDICTIVE MAINTENANCE
Reduce the number of unexpected events by optimizing the maintenance plans and costs caused by failures.

Predictive analytics in science and engineering is undergoing an impressive growth. More and more companies are adapting that technique into their daily operations, as it allows not only to manage any problem that could arise in the present but also to be prevented for future unexpected events.

Maintenance picture

Neural Designer has been conceived to build complex predictive models that represent a broad range of variables associated with the failure of equipment.

Neural Designer allows you to easily configure the neural network and train it in order to elaborate an association model that can identify anomalies in the operation of any of the areas. This analysis is performed by using a data set containing the information about the functioning of the equipment. It also reports the mathematical expression of the mathematical model so it can be used for future events.

Related solutions:

> Performance optimization.
> Fault detection.
> Quality improvement.