Maintenance & Resilience 2026 LOGO

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DATE
July 15(Wed) - 17(Fri), 2026
VENUE
Tokyo Big Sight East Halls 1, 2, 3

At the TDK booth, we have demonstration units available so you can experience the edgeRX Solution firsthand.

You can see the entire process—from installation to anomaly detection—in action. You’ll be able to experience firsthand just how easy it is to implement this solution, as sensors can be installed quickly and anomaly detection begins immediately. See for yourself how much simpler the setup process is compared to traditional predictive maintenance systems, which used to take weeks or even months to get up and running.

We also offer a hands-on experience with the cloud dashboard. You can try operating the screen yourself to see how data collected from sensors is visualized in real time and experience the ease of use of the problem-location identification and labeling functions. We invite you to experience the intuitive interface that requires no specialized knowledge.

Specialized staff with in-depth product knowledge will be on hand at our booth. For customers considering implementation, we will discuss your on-site challenges and requirements to propose the optimal solution configuration.

Exhibited products (English) TDK will be showcasing its next-generation predictive maintenance solution, “edgeRX Solution.”

edgeRX Solution integrates edge AI sensors, communication networks, and cloud analytics to revolutionarily simplify anomaly detection and predictive maintenance on the manufacturing floor. Conventional predictive maintenance systems have required significant time, cost, and specialized expertise—from sensor selection and installation to the construction of data collection infrastructure and the development of analysis algorithms. This solution was developed as an all-in-one platform that addresses these challenges and can be easily implemented by anyone.

Its most notable feature is its ease of implementation. Simply install sensors on the equipment to be monitored on-site, and after just 30 minutes to an hour of automatic learning, anomaly detection becomes possible. Data acquired by the sensors is transmitted to a gateway via Bluetooth and automatically stored and transferred to the cloud. The data in the cloud is visualized on a dedicated dashboard, making it easy to identify and label problem areas.