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RESEARCH OVERVIEW

RESEARCH

OVERVIEW

RESEARCH OVERVIEW

Advanced Autonomous

Production Systems

(APS)

Our research focuses on the development of advanced

Autonomous Production Systems (APS) envisioned as

the future of smart factories. APS represents a

significant advancement over traditional

production systems, characterized by

increased complexity and a reliance on

high-quality, multimodal data integration.

Predictive Maintenance

Although the predictive maintenance market is continuously expanding, current research primarily

focuses on unimodal data from individual components.

However, APS requires a system-level approach that integrates multimodal data—including images,

time series, and audio—to enhance real-time

diagnostics and predictive capabilities.

AI Integration

We leverage cutting-edge AI, including generative and prescriptive AI models, to address data imbalance and improve the accuracy of system diagnostics, remaining life predictions, and real-time root cause analysis.

Large Language Models (LLM)

Our research also integrates LLMs to interpret and process

vast amounts of complex system data in real time.

By leveraging LLMs, we can automate diagnostics and

reporting, enabling systems to explain failures and

recommend corrective actions in natural language.

This significantly improves communication between

machines and human operators, ensuring the seamless

interpretation and transmission of maintenance insights within the APS framework.

RESEARCH OVERVIEW

Advanced Autonomous
Production Systems (APS)
Our research focuses on the development of advanced Autonomous Production Systems (APS)
envisioned as the future of smart factories. APS represents a significant advancement over traditional production systems, 
characterized by increased complexity and a reliance on high-quality, multimodal data integration.

Predictive Maintenance

Although the predictive maintenance market is continuously expanding,
current research primarily focuses on unimodal data from individual components.
However, APS requires a system-level approach that integrates multimodal
data—including images, time series, and audio—to enhance real-time 
diagnostics and predictive capabilities.

AI Integration

We leverage cutting-edge AI, including generative and prescriptive AI models,
to address data imbalance and improve the accuracy of system diagnostics,
remaining life predictions, and real-time root cause analysis.

Large Language Models (LLM)

Our research also integrates LLMs to interpret and process vast amounts of complex
system data in real time. By leveraging LLMs, we can automate diagnostics and
reporting, enabling systems to explain failures and recommend corrective actions 
in natural language. This significantly improves communication between machines
and human operators, ensuring the seamless interpretation and transmission of
maintenance insights within the APS framework.

GRL

Tel. 02-2220-0473 | Fax. 00-0000-0000
Engineering Center 702-2, Hanyang Univ. 222 Wangsimni-ro, Seongdong-gu, SEOUL, KOREA

Business Registration Number. 206-82-07306