Delivering AI to the Industrial Edge

Elevating Machine Health with Advanced AI insights

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Maximize Uptime By continuously monitoring the health of machines, potential issues can be detected early, allowing for timely maintenance and preventing unexpected breakdowns. Reduce Costs Early detection of problems can prevent costly repairs and extend the lifespan of critical machines, ultimately saving money on maintenance and replacement costs. Optimize Production Real-time data from machine health monitoring helps identify inefficiencies and areas for improvement. This allows for adjustments to be made that optimize machine performance, leading to increased production output.

What is Edge AI?

Real-Time Decision Making: Edge AI refers to the deployment of artificial intelligence algorithms and models directly on edge devices, such as sensors, gateways, and other IoT (Internet of Things) systems. This is critical in applications where immediate action may be required, such as predictive maintenance to prevent machine failures.  

Scalable

Deployments Real-time

Decision Making Privacy

and Security Bandwidth

Efficiency Cost

Efficiency Edge AI supports distributed computing architectures, allowing AI models to be deployed across a network of edge devices. This scalability is beneficial for applications spanning large geographical areas or involving many edge devices. Scalable Deployments Real Time Decision Making By processing data locally, Edge AI enables real-time decision-making capabilities. This is critical in applications where immediate action is required, such as in industrial automation. Scalable

Deployments Real-time

Decision Making Privacy

and Security Bandwidth

Efficiency Cost

Efficiency Scalable

Deployments Real-time

Decision Making Privacy

and Security Bandwidth

Efficiency Cost

Efficiency Privacy and Security Keeping data on the edge device can enhance privacy and security, as sensitive data does not need to be transmitted over networks or stored in centralized cloud servers. Scalable

Deployments Real-time

Decision Making Privacy

and Security Bandwidth

Efficiency Cost

Efficiency Bandwidth Efficiency Edge AI reduces the amount of data that needs to be transmitted to the cloud. This optimizes bandwidth usage and reduces associated resource costs. Scalable

Deployments Real-time

Decision Making Privacy

and Security Bandwidth

Efficiency Cost

Efficiency Cost Efficiency By processing data locally, Edge AI can reduce costs associated with cloud computing resources and data transfer fees.

Latest News

TDK SensEI is excited to announce our participation in Promat 2025, taking place from March 17-20 in Chicago.

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At the Energy LIVE show in Houston, TX, Angela Osborne sat down with OGGN for an insightful podcast episode.

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Revolutionizing Predictive Maintenance with Artificial Intelligence and Machine Learning: From High-Tech to Legacy Systems

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