Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems [electronic resource] / by Yaguo Lei, Naipeng Li, Xiang Li.

Por: Lei, Yaguo [author.]Colaborador(es): Li, Naipeng [author.] | Li, Xiang [author.] | SpringerLink (Online service)Tipo de material: TextoTextoEditor: Singapore : Springer Nature Singapore : Imprint: Springer, 2023Edición: 1st ed. 2023Descripción: XIII, 281 p. 116 illus., 104 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789811691317Tema(s): Machinery | Machinery and Machine ElementsFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 621.8 Clasificación LoC:TA213-215Recursos en línea: Libro electrónicoTexto
Contenidos:
Introduction and Background -- Traditional Intelligent Fault Diagnosis -- Hybrid Intelligent Fault Diagnosis Methods -- Deep Learning-Based Intelligent Fault Diagnosis -- Data-Driven RUL Prediction -- Data-Model Fusion RUL Prediction.
En: Springer Nature eBookResumen: This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at present Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis Provides abundant experimental validations and engineering cases of the presented methodologies.
Star ratings
    Valoración media: 0.0 (0 votos)
Existencias
Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos 1 No para préstamo

Acceso multiusuario

Introduction and Background -- Traditional Intelligent Fault Diagnosis -- Hybrid Intelligent Fault Diagnosis Methods -- Deep Learning-Based Intelligent Fault Diagnosis -- Data-Driven RUL Prediction -- Data-Model Fusion RUL Prediction.

This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at present Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis Provides abundant experimental validations and engineering cases of the presented methodologies.

UABC ; Perpetuidad

Con tecnología Koha