Name: Machine Performance Degradation Assessment. Convex Optimization Models and Their Interpretable Data Fusion Applications
Price: 565.88 PEN
Availability: InStock
Author: Dong Wang;Tongtong, B.E. Yan
ISBN: 9780443440076

portada Machine Performance Degradation Assessment. Convex Optimization Models and Their Interpretable Data Fusion Applications

Machine Performance Degradation Assessment. Convex Optimization Models and Their Interpretable Data Fusion Applications

Formato

Libro Físico

Editorial

Elsevier - Health Sciences Division

Autor

Dong Wang;Tongtong, B.E. Yan

Año

2025

N° páginas

152

Encuadernación

Tapa Blanda

Dimensiones

22.90 x 15.20 cm

ISBN13

9780443440076

Machine Performance Degradation Assessment. Convex Optimization Models and Their Interpretable Data Fusion Applications

Dong Wang;Tongtong, B.e. Yan (Autor) · Elsevier - Health Sciences Division · Tapa Blanda

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Reseña del libro "Machine Performance Degradation Assessment. Convex Optimization Models and Their Interpretable Data Fusion Applications"

Machine Performance Degradation Assessment: Convex Optimization Models and Their Interpretable Data Fusion Applications is an essential resource for industry professionals and researchers seeking to understand the latest trends in performance degradation assessment technologies. This comprehensive guide delves into the fundamental theories of convex optimization models while exploring cutting-edge research methods. Readers will gain valuable insights into interpretable data fusion models and their applications, providing practical and theoretical knowledge to advance their understanding of machine performance degradation. In addition to the core mathematical elements, the book includes advanced techniques for formulating degradation properties into convex optimization models for health index construction. Real-world applications and examples demonstrate how these innovative methods can be applied in practice. By presenting novel concepts and analytical frameworks, this book offers fresh perspectives to help readers navigate the complexities of machine performance degradation assessment.