New Heights in Productivity & Profitability
From creating semiconductors to the assembly of giant industrial machines, FogHorn’s Lightning™ edge intelligence platform enhances manufacturing yields and efficiency using real-time monitoring and diagnostics, streaming analytics, machine learning, and operations optimization. The immediacy of edge intelligence enables automated feedback loops in the manufacturing process as well as predictive maintenance for maximizing the uptime and lifespan of equipment and assembly lines.
Lower maintenance costs, improve asset performance and longevity, and maximize yield.
Reduce repair costs, minimize downtime, maximize output, and improve remediation efficiency.
Improve automation, enhance practitioner collaboration, reduce manual data entry and errors.
Maximize high-value asset output, balance reliability, performance, and cost.
Optimize systems for maximum KPI performance, quickly adjust to changing conditions.
Increasing the yield rate for the manufacturing of semiconductors by reducing the number of false positives and false negative alerts
Due to the very complex process and inherent fluctuations associated with manufacturing semiconductors, semiconductor companies are continually trying to improve their yield rate to reduce costs and increase profit margins. One of the main contributing factors is the high rate of false positives and negatives. Semiconductor companies can use FogHorn’s edge intelligence to quickly identify false positives and false negatives in real-time, before they leave the manufacturing facility.
In the semiconductor industry, often because of IP- and security-related concerns, data generated by the fabrication process is not permitted to be moved to cloud-based systems. FogHorn’s edge intelligence solution can satisfy such a requirement by applying real-time streaming analytics close to the source of the data without a need to move the data to the cloud.
Centrifugal pumps are widely used across all industrial and commercial sectors such as Power, Water, Oil and Gas, Healthcare, Manufacturing, Utilities, and Transportation.
Cavitation is a condition that can occur in centrifugal pumps when there is a sudden reduction in fluid pressure. Pressure reduction lowers the boiling point of liquids, resulting in the production of vapor bubbles if boiling occurs. This is more likely to happen at the inlet of the pump where pressure is typically lowest. As the vapor bubbles move towards the outlet of the pump where pressure is higher, they rapidly collapse (return to a liquid state) resulting in shock waves that can damage pump components.
FogHorn’s edge intelligence can ingest streaming data from pump sensors and apply real-time analytics to identify any significant changes in pressure and alert operations personnel before damage occurs. It can also send a signal to the main system to automatically move the flow of the fluid to a different pump to prevent damage and reduce maintenance and downtime costs associated with pump cavitation.
The digital transformation of the $10-trillion manufacturing industry has started and it is only going to increase in the coming years. As a result, every component of the value-chain generates an increasing amount of data, which advanced analytics can quickly turn into actionable insights that are instantly communicated across the relevant parts of the system.
Using FogHorn’s edge intelligence, factories can predict in real-time when unexpected failures will occur or if part of a machine might malfunction prior to either of these events actually happening. It can also help improve product quality by analyzing sensor data in real-time to identify any values that fall outside of previously defined thresholds, identify root problem causes and, if desired, automatically stop the production of defective parts.
FogHorn’s Edge intelligence can transform real-time machine data into actionable insight related to production efficiency and quality metrics that can be used by plant managers to reduce unplanned downtime, maximize yield, and increase machine utilization.