Book description
The first statistics guide focussing on practical application to process control design and maintenance
Statistics for Process Control Engineers is the only guide to statistics written by and for process control professionals. It takes a wholly practical approach to the subject. Statistics are applied throughout the life of a process control scheme – from assessing its economic benefit, designing inferential properties, identifying dynamic models, monitoring performance and diagnosing faults. This book addresses all of these areas and more.
The book begins with an overview of various statistical applications in the field of process control, followed by discussions of data characteristics, probability functions, data presentation, sample size, significance testing and commonly used mathematical functions. It then shows how to select and fit a distribution to data, before moving on to the application of regression analysis and data reconciliation. The book is extensively illustrated throughout with line drawings, tables and equations, and features numerous worked examples. In addition, two appendices include the data used in the examples and an exhaustive catalogue of statistical distributions. The data and a simple-to-use software tool are available for download. The reader can thus reproduce all of the examples and then extend the same statistical techniques to real problems.
- Takes a back-to-basics approach with a focus on techniques that have immediate, practical, problem-solving applications for practicing engineers, as well as engineering students
- Shows how to avoid the many common errors made by the industry in applying statistics to process control
- Describes not only the well-known statistical distributions but also demonstrates the advantages of applying the large number that are less well-known
- Inspires engineers to identify new applications of statistical techniques to the design and support of control schemes
- Provides a deeper understanding of services and products which control engineers are often tasked with assessing
This book is a valuable professional resource for engineers working in the global process industry and engineering companies, as well as students of engineering. It will be of great interest to those in the oil and gas, chemical, pulp and paper, water purification, pharmaceuticals and power generation industries, as well as for design engineers, instrument engineers and process technical support.
Table of contents
- Cover
- Title Page
- Preface
- About the Author
- Supplementary Material
-
Part 1: The Basics
- 1 Introduction
- 2 Application to Process Control
- 3 Process Examples
- 4 Characteristics of Data
-
5 Probability Density Function
- 5.1 Uniform Distribution
- 5.2 Triangular Distribution
- 5.3 Normal Distribution
- 5.4 Bivariate Normal Distribution
- 5.5 Central Limit Theorem
- 5.6 Generating a Normal Distribution
- 5.7 Quantile Function
- 5.8 Location and Scale
- 5.9 Mixture Distribution
- 5.10 Combined Distribution
- 5.11 Compound Distribution
- 5.12 Generalised Distribution
- 5.13 Inverse Distribution
- 5.14 Transformed Distribution
- 5.15 Truncated Distribution
- 5.16 Rectified Distribution
- 5.17 Noncentral Distribution
- 5.18 Odds
- 5.19 Entropy
- 6 Presenting the Data
- 7 Sample Size
- 8 Significance Testing
- 9 Fitting a Distribution
- 10 Distribution of Dependent Variables
- 11 Commonly Used Functions
- 12 Selected Distributions
- 13 Extreme Value Analysis
- 14 Hazard Function
- 15 CUSUM
- 16 Regression Analysis
- 17 Autocorrelation
- 18 Data Reconciliation
- 19 Fourier Transform
-
Part 2: Catalogue of Distributions
-
20 Normal Distribution
- 20.1 Skew‐Normal
- 20.2 Gibrat
- 20.3 Power Lognormal
- 20.4 Logit‐Normal
- 20.5 Folded Normal
- 20.6 Lévy
- 20.7 Inverse Gaussian
- 20.8 Generalised Inverse Gaussian
- 20.9 Normal Inverse Gaussian
- 20.10 Reciprocal Inverse Gaussian
- 20.11 Q‐Gaussian
- 20.12 Generalised Normal
- 20.13 Exponentially Modified Gaussian
- 20.14 Moyal
- 21 Burr Distribution
- 22 Logistic Distribution
- Chapter 23: Pareto Distribution
- 24 Stoppa Distribution
- 25 Beta Distribution
- 26 Johnson Distribution
- 27 Pearson Distribution
- 28 Exponential Distribution
- 29 Weibull Distribution
- 30 Chi Distribution
- 31 Gamma Distribution
- 32 Symmetrical Distributions
-
33 Asymmetrical Distributions
- 33.1 Benini
- 33.2 Birnbaum–Saunders
- 33.3 Bradford
- 33.4 Champernowne
- 33.5 Davis
- 33.6 Fréchet
- 33.7 Gompertz
- 33.8 Shifted Gompertz
- 33.9 Gompertz–Makeham
- 33.10 Gamma‐Gompertz
- 33.11 Hyperbolic
- 33.12 Asymmetric Laplace
- 33.13 Log‐Laplace
- 33.14 Lindley
- 33.15 Lindley‐Geometric
- 33.16 Generalised Lindley
- 33.17 Mielke
- 33.18 Muth
- 33.19 Nakagami
- 33.20 Power
- 33.21 Two‐Sided Power
- 33.22 Exponential Power
- 33.23 Rician
- 33.24 Topp–Leone
- 33.25 Generalised Tukey Lambda
- 33.26 Wakeby
- 34 Amoroso Distribution
- 35 Binomial Distribution
- 36 Other Discrete Distributions
-
20 Normal Distribution
- Appendix 1: Data Used in Examples
- Appendix 2: Summary of Distributions
- References
- Index
- End User License Agreement
Product information
- Title: Statistics for Process Control Engineers
- Author(s):
- Release date: October 2017
- Publisher(s): Wiley
- ISBN: 9781119383505
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