The FactoryTalk Analytics LogixAI module makes predictive analytics more accessible to help more workers make better production decisions
Rockwell Automation, a leading company dedicated to industrial automation and information, announced the launch of a new FactoryTalk Analytics LogixAI module to enhance industrial production.
Previously known as Project Sherlock, the new module leverages artificial intelligence (AI) to identify anomalies in production and send alerts to workers so they can intervene or investigate, as necessary.
With this new module, industrial workers can easily utilise the data collected from their equipment that can help them predict production issues and improve processes with their existing automation and control skill set, the company said in a statement.
Briefing about the new module, Jonathan Wise, Product Manager, Rockwell Automation, said, “The FactoryTalk Analytics LogixAI module makes predictive analytics more accessible to help more workers make better production decisions.”
“The module learns your ControlLogix application and tells operators and technicians when things are changing in unexpected ways. This can help them get ahead of product quality issues and protect process integrity,” he added.
Positive implications for Indian businesses
Dilip Sawhney, Managing Director, Rockwell Automation India, said, “Factory Talk Analytics LogixAI has positive implications for Indian businesses in any sector. By providing guidance based on available data, it will enable operators to help to ensure smooth running of processes or machinery.”
He further said, “Our customers can empower employees to troubleshoot and solve problems as they arise or even before, reducing downtime and ultimately contributing positively to the company’s bottom line.”
The FactoryTalk Analytics LogixAI module is the latest addition from Rockwell Automation to its FactoryTalk Analytics platform. The platform comprises FactoryTalk Analytics for Devices that learns about the structure of an automation system to inform workers regarding the problems with individual devices. The new module expands on this by learning about an automation system’s application and helping identify anomalies with its overall function.
