Yokogawa Unveils Equipment/Quality Predictive Detection Tool
Yokogawa has developed the Equipment/Quality Predictive Detection Tool, an easy-to-use AI-based software application for the recorders and data loggers
Yokogawa Electric Corporation has launched 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.
With this software, even users who are not AI specialists will be able to build their 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.
Features
Predictive detection model can be created by AI based on existing record data without specialized knowledge
A predictive detection model can be created by importing past data into the software and simply flagging it as normal or abnormal, without needing to rely on an AI expert or consultant with knowledge about machine learning, algorithms, etc. Data recorded with Yokogawa and other companies’ products can be used. Simulations can be run in advance to see how the AI assesses the data.
Using the predictive detection model, an equipment and quality predictive detection system can be easily built
By loading the predictive detection model created by this software into the SMARTDAC+ on-site, an equipment and quality anomaly predictive detection system can be constructed. The degree of equipment deterioration can be confirmed before failure by checking the health scores. These health scores enable operators to be informed by alarm or e-mail when equipment needs maintenance, minimizing the likelihood of an unexpected breakdown that can impact production activities.
Available in both cloud and offline versions
The Equipment/Quality Predictive Detection Tool will be available as both a cloud and an offline version. The equipment and quality anomaly predictive detection system can be built using either version. The cloud version is more easily available and does not require any installations on a PC.
