The many facets of PdM: Battery manufacturer uses GenAI to power its data operations

The many facets of PdM: Battery manufacturer uses GenAI to power its data operations

Sept. 30, 2024
In this predictive maintenance case study, an energy storage company uses generative AI to understand complex industrial data.

The medley of predictive maintenance (PdM) strategies for improving machine health is growing larger and more powerful, whether using classic portable tools for non-critical asset inspection rounds and on-site problem verification and troubleshooting, or advanced technologies such as the IIoT, cloud, and AI and ML algorithms. 

Leaders and analysts who go on record by documenting improvements gained from predictive maintenance initiatives provide a window into the immense potential of today’s enabling technologies. This article is one of seven diverse case studies that illustrate some of the many PdM methods and applications employed today. 

The other case studies include:


Challenge: A thermal energy storage solution from Kyoto Group, called the Heatcube, is designed to help reduce the CO2 footprint for industrial process heat by capturing and storing energy from solar and wind sources. Kyoto needed a solution to power its data operations and enable its customers to optimize the operation and maintenance of Heatcube.

Solution: The company chose to integrate its cloud-based DataOps platform with the Cognite Data Fusion (CDF) industrial DataOps platform from Cognite. CDF’s use of Generative AI makes it easier for decision makers to access and understand complex industrial data. Kyoto’s solution also integrates real-time operational data from the Heatcube Battery Management System with various engineering data sources, facilitating product improvements by its engineering team.

Results: The first iteration of Kyoto's DataOps platform is “designed, tailored, and tested” to oversee operations of Heatcube installations at multiple locations. “The integration will empower our customers to streamline data management and cost optimize operations, enabled by a foundation of best-in-class preventive and predictive maintenance,” observes Gustavo Zaera, head of digital acceleration at Kyoto Group.

About the Author

Sheila Kennedy | CMRP

Sheila Kennedy, CMRP, is a professional freelance writer specializing in industrial and technical topics. She established Additive Communications in 2003 to serve software, technology, and service providers in industries such as manufacturing and utilities, and became a contributing editor and Technology Toolbox columnist for Plant Services in 2004. Prior to Additive Communications, she had 11 years of experience implementing industrial information systems. Kennedy earned her B.S. at Purdue University and her MBA at the University of Phoenix. She can be reached at [email protected] or www.linkedin.com/in/kennedysheila.

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