When it comes up to integrating AI within already working core processes and business, one can be struck by fear and be reticent to change. A simple and harmless step towards reaching full automation is to monitor production pipelines and store data. The next step involves constructing dashboards containing accurate metrics and maintenance predictions, thus achieving AI in Downtime Prediction.
AI in Data Forecasting
By tracking circumstances, data stability and applicability, the insights become more relevant and remain significant for much longer. Being able to accurately predict future data allows for deeper delving into data analysis. The end result is the production of much more qualitative business insights and dashboards. Knowing some of the future data might help in ameliorating or even fully preventing big catastrophes waiting around the corner to happen.
Having access to a live data stream of certain metrics (such as voltage) creates a suitable environment for building AI that forecast future values of that metric.
In figure A, two graphs are put head to head in order to demonstrate the usability of an AI used for Voltage Forecasting. The left side of the plot contains past values of voltage. A voltage forecasting AI predicts future possible values with a margin error, displayed on the right part.
AI in Downtime Prediction
A machine is not perfect, and eventually it will stop functioning and require maintenance. Machine downtime periods can be very hurtful to businesses. The costs can go to very large amounts, especially when the maintenance process will take a few months.
Building a large and broad dataset of information about a specific machine usually means aggregating some specific machine data, metric time data and forecasts. This information can be further augmented with past maintenances periods in order to train an AI to predict future downtime risks.
The red peaks from the left part of the figure (B) represent past critical errors which put the machine on downtime. The orange values, from the right, represent future risk values of critical errors happening. A downtime prediction AI produces the risk values from processing already known data.
The graphs from above are constantly updating and moving with the timeline in a live scenario. Fully automating the downtime prediction system will require an error threshold in order to trigger a message or an alarm.
Conclusions
There are many ways in which companies adapt to the new standard of Industry 4.0. Many of them can be very demanding and can be harmful to past systems. Harmless alternatives, such as predictive maintenance, can be safely integrated within the system and represent a strong first step.
Implementing an AI in Downtime Prediction requires read access to some live data streams. This data has various applications in data science and analytics. Some important ones are the processes building the components necessary to fully automate predicting downtime.
By having an automated pipeline for predictive maintenance, the business will have lower productive losses and will require less human labor in order to function.
Final words
In this blog post, the AiRobot team tries to reach out with its work.
In the meantime, more updates are in the works with an attempt at covering as many AI use cases as possible.
Make sure to follow the news!
Further readings
More information about predictive maintenance is available here.
Recent posts from us:
