Artificial Intelligence on Industrial Systems, monitors the plant health parameters, predicts outputs, incoming failures and schedules the required maintenance operations by detecting typical patterns in operating data.
It seeks to make the maintenance operations more economically efficient by allowing to use components that would be replaced at regular intervals under standard maintenance programs for far longer.
Our Predictive Maintenance algorithms analyze all the available sensor data and produce a mathematical model (a statistical fingerprint) of the system. When the system departs from this condition, a failure alarm is triggered and a specific analysis is made to classify the incoming anomaly.
1.Cut maintenance costs
3.Better service to customers
4.Switch from Fixed to Recurrent Costs
Artificial Intelligence can watch thousands of sensor reading at a time to predict System Failures or to Detect Anomalies.
The most common way used today to supervise machines is to create Dashboards to be supervised by human operators.
This approach has many limits: Time and human attention for example. Moreover humans cannot supervise hundreds of data streams simultaneously.
Artificial Intelligence can control 1000’s of variables at the same time as soon as they are available. Each single variable on its own is meaningless, because the system health information is a complex combination of multiple sensor readings.
In this case the graph on the left shows the raw sensor readings while the graph on the right shows the probability of the failure estimated by a Deep Neural Networks algorithm.
Here the Deep Learning algorithm finds three specific failure modes indicated by the three red circles in the graph on the right.