Analyze Production History To Build A Predictive Model
Predictive analytics software can improve manufacturing performance by predicting inline and offline quality failures, material waste, unplanned downtime and more. The first step is to analyze approximately three to six months of historical, high-frequency process & quality data to establish relationships, ranging from strong correlations to cause-and-effect constraints, between different types of metrics and measurements.
Machine learning technologies will then identify patterns in behavior that have previously led to problems on the factory floor. The patterns are refined into predictive analytics models that are built and validated in the cloud.
Make Predictions Based On Live Production Data
Predictive analytics models are then deployed in the cloud or on the edge which limits connectivity dependency and lowers latency for business-critical applications. Machine learning tools run models against live production data to predict quality failures allowing factory personnel to take corrective action faster. Predictive analytics tools also look for a specific set of conditions that indicate a quality or machine failure may happen.
For example, if temperature and vibration measurements on a machine reach certain levels for a specific amount of time there is an increased likelihood the machine will fail. Models are continuously retrained in the cloud and then automatically updated on the edge when there are significant improvements or changes.
Prevent Issues with Real-Time Alerts
Predictive data analytics power real-time alerts that allow factory personnel to proactively address potential problems. Machine learning tools alert operators or engineers when key parameters are trending towards outer limits to help protect operations and avoid quality failures. Predictive analytics can also be used to alert teams when components with a high-probability of failure, such as pumps, fans and motors, meet certain conditions that signify they’re likely to break. Alerts can be customized based on factory floor conditions or combinations of metrics.
For example, an alert is triggered if line speed drops five times over ten minutes but not if it only drops once. This prevents redundant or irrelevant alerts that are ignored or cause operators to stop production unnecessarily.