Falkonry in action


Our customers, who span multiple industries and applications, are realizing improvements in throughput, quality and yield

Quality improvement

A large chemical manufacturer was challenged by variability in quality and yield. They believed the large volumes of process and machine data collected for different phases in their manufacturing process should help identify problematic batches early, however, traditional efforts had proved ineffective.

Integrating the customer’s process historian to the Falkonry LRS platform enabled rapid analysis of thousands of batch executions. This helped identify hidden patterns and early warning signals for in each phase of the manufacturing process. By providing predictive final quality results early in the manufacturing process, the customer was able to scrap low-quality batches early, increasing process efficiency, quality and yield, while avoiding downstream supply chain issues.

Improved throughput and uptime

A mining company was experiencing frequent, unplanned downtime due to variation in raw materials that impacted a critical process-line machine, costing the company $30,000 per hour. Instrumentation and data collection captured large volumes of operational data, but efforts to turn this into meaningful improvements in operational efficiency fell short.

The Falkonry LRS platform was installed and integrated into the customer’s OSIsoft PI system. Analyzing and correlating the data streams representing motor currents, temperatures and valve settings, with downtime events, enabled the customer to identify patterns leading to the downtime event. This condition monitoring enabled the operations team to take corrective actions and improve machine uptime and throughput.

Predictive maintenance

A semiconductor manufacturer operates complex, expensive equipment that executes many different types of step-based operations daily, and optimizing utilization through predictive maintenance is a high priority. The machines are instrumented to collect operational data every second in the form of sensor readings, control parameters, and other settings. Use and benefits of this data to date had proved ineffective in predicting faults to operators.

The Falkonry LRS platform was installed, and quickly integrated to the manufacturer’s operational datastore containing trace data, quality measures, and inspector and operator log information. A four-month history of data created a model identifying multiple abnormal conditions known to create maintenance indicators, creating alerts in the Falkonry system. 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).

More examples of ready-to-use machine learning

Steelmaker machine learning

Increased throughput through downtime avoidance

A steelmaker discovered patterns in their high-pressure water pump operating data. By recognizing pre-failure conditions they predicted unscheduled downtime.

Jet turbine machine learning

Increased time to value by avoiding unproductive engineering time

A jet turbine manufacturer is able to detect quality defects earlier by augmenting visual inspections with machine learning to determine “good” vs. “bad”. Correlations from identified patterns help identify causes.

Oil and gas refinery machine learning

Improved throughput by avoiding production and material losses

An oil & gas producer experiences weekly shutdowns of its refining operations and $1M+ of production losses. By detecting pre-shutdown patterns, corrective action is taken early to increase yield.

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