The new module continuously monitors equipment parameters and detects anomalies by analyzing changes in their behavior. Powered by a neural network, it learns from historical examples, making it possible to identify virtually any type of anomaly as long as a similar pattern has been observed before. 

Unlike traditional monitoring based on static thresholds, Trend Analysis focuses on how parameters evolve over time. It recognizes subtle deviations across multiple signals, allowing it to detect abnormal operating conditions much earlier than conventional alarm systems. 

The example below shows how nv.ebs identified changes across a group of parameters and alerted the user before the equipment reached a failure state. This gives engineers valuable time to investigate the issue, take corrective action, and prevent unplanned downtime – turning historical operational data into an effective predictive maintenance tool.