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Könyv Zero-Shot Intelligence in Diagnosis and Decision for Energy Systems Chunhui Zhao

Zero-Shot Intelligence in Diagnosis and Decision for Energy Systems

From Unseen to Understood

Szerző: Chunhui Zhao, Jiaqi Yue
Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: Springer, Berlin
Elérhetőség: Könyvújdonság
Küldés 04. 04. 2027
60 358 Ft
This book resides at the intersection of industrial informatics, artificial intelligence, and predic...

Információk a könyvről

Szerző
Chunhui Zhao, Jiaqi Yue
Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2027
EAN
9789819286584
Enbook ID
54320698
Kiadó
Springer, Berlin
Méretek
155 x 235

Teljes leírás

This book resides at the intersection of industrial informatics, artificial intelligence, and predictive maintenance. It pioneers a paradigm shift from data-driven fault diagnosis to knowledge-data fusion intelligence, leveraging zero-shot learning (ZSL) to address the critical challenge of diagnosing unseen faults in industrial systems where labeled data is scarce or absent. It is the first systematic integration of ZSL, LLMs, and federated learning for industrial diagnostics and provides roadmap to replace black-box AI with interpretable, trustworthy systems-critical for safety compliance. The book introduces cutting-edge methodologies that bridge theoretical AI and industrial practicality. They tackle industry's "cold-start" problem (no fault samples), and enable lifelong learning systems-key for evolving industrial environments. The section on application scenarios combines practical cases from power generation system, chemical processes, and Industrial Internet of Things (IIoT) to analyze the engineering challenges faced in implementing these technologies. Additionally, this book defines industrial AI by merging knowledge embedding, dynamic adaptation, and explainable decision-making. It transitions fault diagnosis from anomaly detection to cognitive reasoning-where machines "understand" unseen faults as human experts would. This book is the cornerstone of next-generation industrial safety: scalable, transparent, and resilient. The book is intended for academic researchers (AI/industrial informatics), industrial R&D engineers, and graduate students.