Name: Industrial Data Analytics for Diagnosis and Prognosis With r - a Random Effects Modelling Approach (en Inglés)
Price: 175330.00 CLP
Availability: InStock
Author: Shiyu Zhou; Yong Chen
ISBN: 9781119666288

portada Industrial Data Analytics for Diagnosis and Prognosis With r - a Random Effects Modelling Approach (en Inglés)

Formato

Libro Físico

Editorial

Wiley

Autor

Shiyu Zhou; Yong Chen

Año

2021

Idioma

Inglés

N° páginas

416

Encuadernación

Tapa Dura

ISBN13

9781119666288

N° edición

Categorías

Recursos humanos

Industrial Data Analytics for Diagnosis and Prognosis With r - a Random Effects Modelling Approach (en Inglés)

Shiyu Zhou; Yong Chen (Autor) · Wiley · Tapa Dura

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Reseña del libro "Industrial Data Analytics for Diagnosis and Prognosis With r - a Random Effects Modelling Approach (en Inglés)"

Discover data analytics methodologies for the diagnosis and prognosis of industrial systems under a unified random effects model In Industrial Data Analytics for Diagnosis and Prognosis with R - A Random Effects Modelling Approach, distinguished engineers Drs. Shiyu Zhou and Yong Chen deliver a rigorous and practical introduction to the random effects modeling approach for industrial system diagnosis and prognosis. In the book’s three parts, general statistical concepts and useful theory are described and explained, as are diagnostic methods and methods of prognosis. Throughout, R code is provided for all major examples, enabling the quick implementation of the techniques covered in the book. The accomplished authors describe and model fixed effects, random effects, and variation in univariate and multivariate datasets and cover the application of the random effects approach to diagnosis of variation sources in industrial processes. They offer a detailed performance comparison of different methods before moving on to the application of the random effects approach to failure prognosis in industrial processes and systems. In addition to presenting the joint prognosis model, which integrates the survival regression model with the mixed effects regression model, the book also offers readers: A thorough introduction to describing variation of industrial data, including univariate and multivariate random variables and probability distributions An exploration of principal components of variance-covariance structure, including geometric interpretation and the graphing of principal components Discussions of linear regression models and linear mixed effects models, including parameter estimation and fixed and random effects Rigorous treatments of the diagnosis of variation sources using PCA pattern matching and the Random Effects Model Ideal for senior year undergraduate students and postgraduate students in industrial, manufacturing, mechanical, and electrical engineering, Industrial Data Analytics for Diagnosis and Prognosis with R is also an indispensable guide for researchers and engineers interested in data analytics methods for system diagnosis and prognosis.