HomeData AnalyticsHow Intel is using IIoT edge computing to reduce factory downtime by 300%

How Intel is using IIoT edge computing to reduce factory downtime by 300%

Semiconductor maker Intel has applied predictive maintenance to monitor the health of its fan filter
units (FFUs) in its semiconductor production facilities through deployment of industrial IoT (IIoT) sensors and edge computing. The idea was to alert technicians to potential problems, define a proactive maintenance schedule, and reduce unscheduled downtime.

FFUs filter and clean the air inside industrial machines. Detecting FFU health is typically a manual process, which makes predicting failures hard. They are everywhere on the factory floor.

Fan fiulter units – detecting faults is often a manual process; predicting failure is difficult

“FFUs represent a single process that is small enough in scope yet large enough in impact to demonstrate a return on investment in the factory… They represent a stand-alone process and are exceptional for demonstrating the ROI potential of an edge-computing and cloud-based IIoT predictive-maintenance solution,” explains Intel.

The semiconductor manufacturer wanted to move the analytics to the edge of the network in order to reduce data across the network, and manage security. It also needed to identify the behaviour of each FFU through traditional reporting, including statistics, graphs, and charts, based on summary data.

In the end, it combined with GE Digital to develop a solution based on Intel’s ‘Intel IoT’ gateways and GE’s Predix industrial IoT platform, enabling data processing at the edge. “We evaluated several products, and GE’s Predix met the requirements. It also provided a high degree of security that met our criteria for storing confidential intellectual property data.”

Intel placed an accelerometer at the top of each FFU to measure variations in the fan’s function, creating a baseline for comparing behaviour across the in both the tool and fleet.It also integrated accelerometers with gateways and edge applications in the set-up, and developed machine learning algorithms around.

“This created a baseline performance for each FFU, measured changes, and generated alerts for anomalies and potential problems,” it comments. Summary data was sent to the cloud to give tool owners a view into baselines and trends, and a chance to respond to alerts of anomalies in the system.

IIoT set-up – sensors were attached to each fan unit to measure vibration and transmit data to the edge application

Intel has reduced downtime from FFU failures by 300 per cent over manual inspection. In parallel, it has increased FFU uptime by over 97 per cent, by ordering replacement parts and scheduling maintenance ahead of time. It has also effectively eliminated ‘excursions’, it says, which show changes in the manufacturing process that result in damage to materials.

“The framework we developed for the FFU process creates a foundation that can be used in a variety of other IIoT use cases in the factory. For example, we plan to scale this solution to include detecting anomalies in electro-mechanical devices and other manufacturing processes,” it says.

“Additionally, Intel is now partnering with GE to work with OEMs and suppliers to learn how a partnership approach, along with external domain expertise, can further enhance the value of this IIoT solution across Intel’s supply chain.”

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