Machine learning algorithms have the ability to learn from data and to make predictions based on that data. Predictive analytics extract information from data sets in order to determine patterns and predict future outcomes and trends. Avnet Silica deliver solutions both for cloud and edge applications for industrial and consumer markets.
Supported machine learning tasks
Condition monitoring monitors parameters of the condition in machinery in order to identify changes of those parameters, which can be an indication for a fault or failure of the machinery. It helps to determine the condition of in-service equipment in order to estimate when maintenance should be performed and results in a reduction in unplanned downtime costs because of failure.
Several condition monitoring techniques are supported:
- Predictive maintenance to evaluate the condition of equipment by performing continuous condition monitoring. The goal is to perform maintenance before the equipment fails or loses performance. Predictive maintenance benefits from preventive maintenance because it relies on the actual condition of equipment based on measured sensor parameters from multiple sensors.
- Anomaly detection to identify rare items, events or observations, which raise suspicions by differing significantly from the majority of the data. Detecting anomalies over time helps to identify induced problems such as structural defects.
- Prescriptive maintenance to automate complex decisions not only by detection and predicting upcoming issues or failures, like with predictive maintenance, but also by making advanced suggestions to reduce downtime or even to improve production.
Solutions highlights
