Hongpeng Yin, Li Cai, Peng Zhang

Broschiertes Buch

Industrial Fault Diagnosis and Remaining Useful Life Prediction

Cross-Domain, Zero-Sample, and Degradation Modeling Methods

Beschreibung

Industrial Fault Diagnosis and Remaining Useful Life Prediction introduces zero-sample learning methods that enable fault diagnosis and Predict Remaining Useful Life (RUL) without the need for labelled fault data. This is particularly valuable in industrial settings where labelled data is scarce or unavailable. Offers step-by-step guidance on implementing zero-shot learning models using real industrial data, reducing the learning curve for practitioners; includes real-world industrial case studies to demonstrate the application of zero-sample learning techniques in various industries, such as manufacturing, energy, and transportation. Such case studies provide readers with actionable insights and practical solutions. The book covers advanced methodologies for predicting the remaining useful life of industrial equipment, supporting readers in optimizing maintenance schedules, reducing downtime and extending the lifespan of critical assets. Covers state-of-the-art algorithms, including deep learning, transfer learning and domain adaptation, tailored for zero-sample scenarios. These tools empower readers to develop robust fault diagnosis and RUL prediction systems, enhancing predictive maintenance capabilities and ensuring the reliability of industrial systems

Autor

Professor Hongpeng Yin is based at the School of Automation, Chongqing University in China. His current research interests mainly include data-driven process monitoring and fault diagnosis, pattern recognition, and data mining

Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 200
• Erscheinungstermin: 1. Februar 2026
• Englisch
• ISBN-13: 9780443442919
• ISBN-10: 0443442916
• Artikelnr.: 74783337