Submission Deadline: 31 January 2025 View: 313 Submit to Special Issue
Prof. David He, The University of Illinois at Chicago, United States
Prof. Yongzhi Qu, The University of Utah, United States
Dr. Miao He, Siemens Corporation. United States
Prognostics and Health Management (PHM) is a critical field focused on the prediction and prevention of system failures, ensuring the reliability and safety of engineering systems. As engineering systems become increasingly complex, the need for advanced methods to predict failures and optimize maintenance strategies has never been greater. Large Language Models (LLMs), a cutting-edge development in artificial intelligence, offer transformative potential in this domain. LLMs, such as GPT-4 and beyond, have demonstrated remarkable proficiency in understanding and generating human-like text based on vast datasets. Their application extends beyond natural language processing to various technical fields. In PHM, LLMs can process and analyze large volumes of unstructured data, such as maintenance logs, sensor data, and operational records, to uncover patterns and insights that traditional methods might miss.
This special issue aims to explore the integration of LLMs into PHM, highlighting their capability to revolutionize predictive maintenance, diagnostics, and decision-making processes. It will showcase novel methodologies, algorithms, and frameworks leveraging LLMs to enhance prognostics and health management systems.
Key areas of interest include, but are not limited to:
· Predictive Maintenance: Utilizing LLMs to forecast equipment failures and schedule maintenance activities proactively.
· Fault Diagnosis: Implementing LLMs for real-time fault detection and diagnosis through natural language analysis of maintenance logs and reports.
· Prognostics: Implementing LLMs for real-time prediction of remaining useful life of mission critical systems/components through natural language analysis of maintenance logs and reports.
· Data Integration: Combining structured and unstructured data sources using LLMs to provide comprehensive insights into system health.
· Human-Machine Collaboration: Enhancing human decision-making in PHM through intuitive interfaces and intelligent assistants powered by LLMs.
· Case Studies and Applications: Real-world implementations of LLM-based solutions in various engineering sectors, demonstrating their practical impact and benefits.
We invite researchers, practitioners, and industry experts to submit their original research articles, review papers, and case studies that address the application of LLMs in PHM. Contributions should demonstrate significant advancements in the field and provide practical insights into the benefits and challenges of implementing LLM-based solutions in real-world scenarios.
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