rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Statistical monitoring of complex multivariate processes
    sub title: with applications in industrial process control
  name:
    name part: Krüger.
    name part: Dr
    role:
      role term: creator
  name:
    name part: Xie, Lei.
  name:
    name part: D. R.
  name:
    name part: Uwe.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: enk
    place:
      place term: Chichester [England]
    place:
      place term: Hoboken, N.J
    publisher: Wiley
    date issued: 2012
    edition: 1版ition
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (472 p.)
  abstract: "The book summarises recent advances in statistical-based process monitoring of complex multivariate process systems"--
  table of contents: Machine generated contents note: Preface Introduction I Fundamentals of Multivariate Statistical Process Control 1 Motivation for Multivariate Statistical Process Control 1.1 Summary of Statistical Process Control 1.1.1 Roots and Evolution of Statistical Process Control 1.1.2 Principles of Statistical Process Control 1.1.3 Hypothesis Testing, Type I and II errors 1.2 Why Multivariate Statistical Process Control 1.2.1 Statistically Uncorrelated Variables 1.2.2 Perfectly Correlated Variables 1.2.3 Highly Correlated Variables 1.2.4 Type I and II Errors and Dimension Reduction 1.3 Tutorial Session 2 Multivariate Data Modeling Methods 2.1 Principal Component Analysis 2.1.1 Assumptions for Underlying Data Structure 2.1.2 Geometric Analysis of Data Structure 2.1.3 A Simulation Example 2.2 Partial Least Squares 2.2.1 Assumptions for Underlying Data Structure 2.2.2 Deflation Procedure for Estimating Data Models 2.2.3 A Simulation Example 2.3 Maximum Redundancy Partial Least Squares 2.3.1 Assumptions for Underlying Data Structure 2.3.2 Source Signal Estimation 2.3.3 Geometric Analysis of Data Structure 2.3.4 A Simulation Example 2.4 Estimating the Number of Source Signals 2.4.1 Stopping Rules for PCA Models 2.4.2 Stopping Rules for PLS Models 2.5 Tutorial Session 3 Process Monitoring Charts 3.1 Fault Detection 3.1.1 Scatter Diagrams 3.1.2 Nonnegative Quadratic Monitoring Statistics 3.2 Fault Isolation and Identification 3.2.1 Contribution Charts 3.2.2 Residual-Based Tests 3.2.3 Variable Reconstruction 3.3 Geometry of Variable Projections 3.3.1 Linear Dependency of Projection Residuals 3.3.2 Geometric Analysis of Variable Reconstruction 3.4 Tutorial Session II Application Studies 4 Application to a Chemical Reaction Process 4.1 Process Description 4.2 Identification of a Monitoring Model 4.3 Diagnosis of a Fault Condition 5 Application to a Distillation Process 5.1 Process Description 5.2 Identification of a Monitoring Model 5.3 Diagnosis of a Fault Condition III Advances in M…
  note: Uwe Kruger and Lei Xie.
  note: Description based upon print version of record.
  note: Includes bibliographical references and index.
  note: English
  subject:
    topic: Multivariate analysis
  classification: QA278 .K725 2012
  classification: 519.5/35
  classification: MAT029020
  related item:
    title info:
      title: Statistics in practice
  related item:
    title info:
      title: Statistics in Practice
  identifier: 9786613862228
  identifier: 9781283549776
  identifier: 1283549778
  identifier: 9780470517253
  identifier: 0470517255
  identifier: 9780470517246
  identifier: 0470517247
  identifier: 9781118381267
  identifier: 1118381262
  identifier: 2012016445
  record info:
    record content source: MiAaPQ
    record creation date: 120423
    record change date: 20260630163056.0
    record identifier: 99370675295206441