Predictive Maintenance for industrial equipment

The implementation of a predictive maintenance system enables the anticipation of a failure or incident in industrial machines by analyzing the data of their operation. Predicting the moment when the equipment might fail allows to: avoid unplanned stoppages, extend production cycles between maintenance work downtime, keep repair time to a minimum, extend the components service life and reduce spare parts stock.

The predictive maintenance tool enables monitoring and acting according to the data

• If your infrastructure and machinery are sensor-equipped, you can know about their performance and status even in real time, which can be the starting point for your predictive maintenance strategy.
• The extraction and configuration of records that require a limited amount of historical (non-erroneous) and current operational data.
• All the information ingested feeds the data model in the cloud, where it is retrained to improve the reliability and accuracy of the predictions
• The dashboard view enables the configuration and management of alarms and the display of information.

And that brings the following benefits:

• Cost savings: preventing errors plays a central role to avoid costs incurred in system downtimes or replacement of machinery. The cloud approach enables pay-per-use instead of licence models.
• Service improvement: avoid system failures and prolonged downtimes, in favour of a better service and greater system efficiency. For this purpose, configuration and customization of appropriate alarms to each system are the key tool.
• Increase in the asset's lifetime: proper maintenance lengthens the life of machinery. Compared to the systematic routine time-based maintenance model, predictive maintenance has the advantage that in most cases no major repairs have to be carried out.
• Learn from your own data: there is no need for a large amount of previous data. It enables the analysis of behaviour patterns from a small data set of historical data and data of the current status. Accelerates time-to-market: it deploys an architecture up to 75% faster than a solution that starts from scratch, but retains all the advantages of customization and individual configuration in data analysis.