Yokogawa Electric Corporation has announced the release of its Equipment/Quality Predictive Detection Tool. This addition to the OpreX Data Acquisition family is an artificial intelligence (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.

According to the company, with 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 reportedly help them improve production efficiency by identifying equipment defects and deteriorating quality in their plants and other facilities at an early stage.

Source: Yokogawa Electric CorporationSource: Yokogawa Electric Corporation

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.

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 and algorithms. 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.

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.

Example applications for the Equipment/Quality Predictive Detection Tool include:

Managing temperatures and pressures in tire production (vulcanization)
Pressure leaks due to packing deterioration and so on are an issue with the vulcanizers that are used to apply heat and pressure to tire rubber. By using the Equipment/Quality Predictive Detection Tool to monitor changes in a health score that is generated based on a vulcanizer's pressure readings, signs of packing deterioration can be detected at an early stage.

Managing the thermal treatment of aerospace and automotive parts
In the thermal treatment of aircraft and automotive parts, issues like bad burners and inadequate sealing can lead to furnace downtime and poor product quality. The Equipment/Quality Predictive Detection Tool will enable signs of temperature problems to be detected before an alarm is triggered, so users can avoid product loss and predict when to perform maintenance.

Managing sterilization of food and drug products
With the vacuum sealing of food and drug products after they have been sterilized, unexpected equipment breakdowns can bring the production line to a halt and result in product loss. The Equipment/Quality Predictive Detection Tool will enable the detection of loose valves and packing deterioration before an alarm is triggered, so users can prevent equipment breakdowns and reduce product loss.

For more information, visit the Yokogawa website.

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