Predictive Maintenance

Machine learning solutions

Predictive maintenance logo

These techniques are designed to determine the actual condition of equipment, in order to predict when maintenance should be performed.

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

Objectives

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

Our purpose is to build complex predictive models that represent a broad range of variables associated with the failure of plants.

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.

To do so, they analyse data from outside and inside the plant. If during operation they detect a behaviour deviation, a flag is raised.

Advanced analytics

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

In this regard, neural networks can early spot potential failures of plants in order to fix them before they happen.

Benefits

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

Predictive maintenance reduces costs and risk of engineering companies by anticipating failures of their technical equipment.

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.

Implementation

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.

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 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.

Conclusions

Predictive analytics in science and engineering is undergoing an impressive growth.

More and more companies are adapting that technique into their daily operations. Indeed, it allows not only to manage any problem that could arise in the present but also to be prevented for future unexpected events.

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

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