Industrial Data Analytics for Diagnosis and Prognosis with R - A Random Effects Modelling Approach

افزودن به بوکمارک اشتراک گذاری 0 دیدگاه کاربران 5 (0)

مشارکت: عنوان و توضیح کوتاه هر کتاب را ترجمه کنید این ترجمه بعد از تایید با نام شما در سایت نمایش داده خواهد شد.

ترجمه این کتاب مشاهده همه کتاب های ترجمه نشده

از خودتان درباره کتاب توضیح کوتاهی بنویسید

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.

لغو

گزارش تخلف

مغایرت عنوان کتاب با توضیحات و تصویر جلد
عدم رعایت حقوق نشر طبق قوانین جمهوری اسلامی ایران
محتوای مستهجن
توهین به قومیتها و محتوای نژادپرستی
توهین به اقیلیتهای مذهبی
اهانت به مقامات سیاسی و مذهبی
اشاعه ی کودک آزاری
سایر موارد

بستن

فرمت کتاب

ebook

زبان

english

تاریخ انتشار

2021

نویسنده

Shiyu Zhou

ناشر

Wiley

شابک

1119666287

20,000 تومان

خرید کتاب مطالعه نمونه درخواست ترجمه کتاب

خرید کتاب Industrial Data Analytics for Diagnosis and Prognosis with R - A Random Effects Modelling Approach

Industrial Data Analytics for Diagnosis and Prognosis with R - A Random Effects Modelling Approach

گزارش تخلف

فرمت کتاب

ebook

زبان

english

تاریخ انتشار

2021

نویسنده

ناشر

شابک

کتاب های مرتبط

Empirical likelihood method in survival analysis

Dynamic systems models : new methods of parameter and state estimation

9780803941076

The Likelihood Principle

The First Erich L. Lehmann Symposium

Optimality

Optimality : the second Erich L. Lehmann Symposium

Optimality : the third Erich L. Lehmann Symposium

Handbook of Chemical Mass Transport in the Environment

A Comparison of the Bayesian and Frequentist Approaches to Estimation

Optimal Estimation of Dynamic Systems

Optimal Estimation of Dynamic Systems

Introduction to Direction-Of-Arrival Estimation

Missing Data and Small-Area Estimation

Jahrbuch Fur Handlungs- Und Entscheidungstheorie

Técnicas de muestreo estadistico

Statistical Paradigms

Schliessende Statistik : Schätzen und Testen, mit vollständig gelösten Aufgaben

Density estimation for statistics and data analysis

Recent Advances in Estimating Nonlinear Models

Applied Statistical Inference

دیدگاه کاربران