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Yokogawa to Introduce Equipment/Quality Predictive Detection Tool

Yokogawa has provided many customers data consulting services and technologies.

Yokogawa Electric Corporation to announce the Equipment/Quality Predictive Detection Tool. This addition to the OpreX™ Data Acquisition family is an AI-based tool for building equipment and quality anomaly predictive detection systems for GX series, GP series, and GM series SMARTDAC+™ paperless recorders and data loggers.

yokogawaWith this software, even users who are not AI specialists will be able to build their own equipment and quality anomaly predictive detection systems for manufacturing sites. It will help them improve production efficiency by identifying equipment defects and deteriorating quality in their plants and other facilities at an early stage.

Recorders and data loggers are used at production and development sites in all kinds of industries to collect, display, and record data on voltage, current, temperature, flow rate, pressure, and other variables. As a leading company in this field, Yokogawa has provided many customers with data consulting services and technologies such as machine learning that can help them predict problems with plant equipment and product quality, and analyze and identify causes.

In recent years there has been a rising demand for AI-based solutions to improve production efficiency in plants. However, the hurdles for the application of AI are high as this requires significant expertise in specialized fields like data science. To address this need, Yokogawa has developed the Equipment/Quality Predictive Detection Tool, an easy-to-use AI-based software application for the recorders and data loggers that are commonly used in industry. To use this tool, no specialized AI expertise or consulting is required.


Nitisha Dubey

I am a Journalist with a post graduate degree in Journalism & Mass Communication. I love reading non-fiction books, exploring different destinations and varieties of cuisines. Biographies and historical movies are few favourites.

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