Industrial Predictive Maintenance
Detect anomalies by analyzing data and predict future industrial equipment failures before they happen.
The implementation of a predictive maintenance system enables the anticipation of a failure or an incident in industrial machines by analyzing the data of their operation, whereby a possible performance degradation is detected in time before it occurs. A large amount of data history, both of correct operation and of failures, is typically required to train the models that detect these anomalies. However, this solution developed by Keepler removes this barrier and can work with a reduced data set and current data of correct operation.
For those companies that want to improve their service and save costs, this is a cloud-based solution that relies on machine learning in order to continuously optimize the predictive algorithm. Fully customizable to give the best answer to performance and maintenance issues.
- 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.
Rely on an A.I and cloud-based solution that combines the benefits of the cloud and the advantage of preventive maintenance.
Cost savings
Preventing errors plays a central role to avoid costs incured 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 favor 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 useful life
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 behavior 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.
Contact us and we will start working together.