Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 35 of its 122 references that are in the map. First posted on arXiv 11 Dec 2023.
Builds on
35 of 122 references.
References · 35
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2023GPT-4 Technical Reportcited by 18,132
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2023Segment Anythingcited by 8,158
- 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
- 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
23 more
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
- 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,037
- 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
- 2022VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Trainingcited by 1,305
- 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
- 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
- 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 382
- 2020Visual Transformers: Token-based Image Representation and Processing for Computer Visioncited by 333
- 2016Learning Convolutional Neural Networks for Graphscited by 314
- 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Modelscited by 244
- 2015Semi-supervised Sequence Learningcited by 198
- 2023Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imagingcited by 161
- 2022Multi-Modal Knowledge Graph Construction and Application: A Surveycited by 74
- 2016Unsupervised Pretraining for Sequence to Sequence Learningcited by 47
- 2021A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learningcited by 31
- 2023AI-GOMS: Large AI-Driven Global Ocean Modeling Systemcited by 26
- 2022ERNIE-GeoL: A Geography-and-Language Pre-trained Model and its Applications in Baidu Mapscited by 25
- 2021Dual Aspect Self-Attention based on Transformer for Remaining Useful Life Predictioncited by 16
- 2021A novel time-frequency Transformer based on self-attention mechanism and its application in fault diagnosis of rolling bearingscited by 12
- 2021Network representation learning: A macro and micro viewcited by 3
- 2022A survey on the development status and application prospects of knowledge graph in smart gridscited by 2
Built on by
Citing papers per year: 2024: 1; 2025: 2.
3 papers.
- 2025Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systemscited by 4
- 2025Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Thingscited by 1
- 2024VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysiscited by 0
- 2025Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systems5 Jul 2025
- 2025Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things26 May 2025
- 2024VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysis3 Sep 2024