• Advanced pattern recognition
• Learning anomaly detection
• Cloud based and on-prem
• Supervised and unsupervised learning
• Topology optimization
There has been a move from talking about Predictive or even Prescriptive Maintenance to Anomaly Detection as industrial companies realize that their data may not be good enough for predictions. This may be from a lack of labeled data1 or that the machines are simply too robust for predictions, i.e. do not break often enough for patterns to emerge.
We see improved maintenance as a continuum and have built solutions that span from unsupervised anomaly detection to predictive maintenance all the way to optimized maintenance, also called prescriptive maintenance. This makes it possible to evolve the solution from anomaly detection to a predictive solution over time as the data matures.
1Labeled data is data that can serve as an example for the AI to learn from, e.g. data that is linked to maintenance events.