Industrial data analytics for diagnosis and prognosis : a random effects modelling approach /

"Today, we are facing a data rich world that is changing faster than ever before. The ubiquitous availability of data provides great opportunities for industrial enterprises to improve their process quality and productivity. Industrial data analytics is the process of collecting, exploring, and...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Zhou, Shiyu, 1970- (Autor), Chen, Yong (Professor of industrial and systems engineering) (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, New Jersey : John Wiley & Sons, Inc., [2021]
Temas:
Industrial engineering > Statistical methods.
Industrial management > Mathematics.
Random data (Statistics)
Estimation theory.
Gestion d'entreprise > Mathématiques.
Données aléatoires (Statistique)
Théorie de l'estimation.
Estimation theory
Industrial engineering > Statistical methods
Industrial management > Mathematics
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Introduction to data visualization and characterization
  • Random vectors and the multivariate normal distribution
  • Explaining covariance structure : principal components
  • Linear model for numerical and categorical response variables
  • Linear mixed effects model
  • Diagnosis of variation source using PCA
  • Diagnosis of variation sources through random effects estimation
  • Analysis of system diagnosability
  • Prognosis through mixed effects models for longitudinal data
  • Prognosis using Gaussian process model
  • Prognosis through mixed effects models for time-to-event data.

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