  Industrial Cyber-Physical Systems (ICPS) integrate the disciplines of
computer science, communication technology, and engineering, and have emerged
as integral components of contemporary manufacturing and industries. However,
ICPS encounters various challenges in long-term operation, including equipment
failures, performance degradation, and security threats. To achieve efficient
maintenance and management, prognostics and health management (PHM) finds
widespread application in ICPS for critical tasks, including failure
prediction, health monitoring, and maintenance decision-making. The emergence
of large-scale foundation models (LFMs) like BERT and GPT signifies a
significant advancement in AI technology, and ChatGPT stands as a remarkable
accomplishment within this research paradigm, harboring potential for General
Artificial Intelligence. Considering the ongoing enhancement in data
acquisition technology and data processing capability, LFMs are anticipated to
assume a crucial role in the PHM domain of ICPS. However, at present, a
consensus is lacking regarding the application of LFMs to PHM in ICPS,
necessitating systematic reviews and roadmaps to elucidate future directions.
To bridge this gap, this paper elucidates the key components and recent
advances in the underlying model.A comprehensive examination and comprehension
of the latest advances in grand modeling for PHM in ICPS can offer valuable
references for decision makers and researchers in the industrial field while
facilitating further enhancements in the reliability, availability, and safety
of ICPS.
