A semiconductor manufacturer operates complex equipment that executes many different types of step-based operations in the course of a day. These machines are expensive, and optimizing utilization through predictive maintenance is a high priority. The machines are instrumented to collect operational execution data every second in the form of sensor readings, control parameters, and other settings – use of this data to improve maintenance and operational efficiency; however, has been very limited. Maintenance is driven by periodic inspection and response to faults presented to operators.
A Falkonry server is installed on-premises, and is connected to the manufacturer’s operational datastore. This datastore captures the following information:
A maintenance engineer identifies a step type that is projected to be a good indicator of machine health. The engineer then executes the following tasks with Falkonry:
The availability of advance warning provided by the Falkonry assessment stream supports early intervention by the maintenance team and results in an improvement in uptime and Overall Equipment Effectiveness (OEE).
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All rights reserved. Falkonry Inc