Machine learning technologies create a digital thread of your production processes. Factory data from machines and sensors is centralized and formatted for consistency, such as unit conversions and unified labeling.
The digital thread provides a contextualized representation of your production line and acts as a roadmap for your predictive maintenance solution.
Machine learning algorithms analyze live production data to detect anomalies in machine behavior that indicate a potential failure; such as a motor temperature that’s too high or a pump pressure that’s too low.
Algorithms also analyze historical data to identify patterns in behavior that have previously led to machine failures. For example, machine jams typically happen when a line speed goes above a certain rate.
Predictive maintenance alerts are generated when machine metrics reach high or low thresholds or meet specific conditions that indicate potential failures. This allows your teams to proactively maintain machines to avoid extended downtime.
Alerts can be customized so an alert is only generated if motor speed drops five times over ten minutes, not if it drops once. This prevents alarm fatigue with ignored alarms or unnecessary production disruptions.
