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E-raamat: Statistics for Process Control Engineers: A Practical Approach

(Whitehouse Consulting)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 10-Aug-2017
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119383529
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 10-Aug-2017
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119383529

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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. 

Preface xiii
About the Author xix
Supplementary Material xxi
Part 1: The Basics 1(314)
1 Introduction
3(2)
2 Application to Process Control
5(6)
2.1 Benefit Estimation
5(2)
2.2 Inferential Properties
7(1)
2.3 Controller Performance Monitoring
7(1)
2.4 Event Analysis
8(1)
2.5 Time Series Analysis
9(2)
3 Process Examples
11(12)
3.1 Debutaniser
11(1)
3.2 De-ethaniser
11(1)
3.3 LPG Splitter
12(5)
3.4 Propane Cargoes
17(1)
3.5 Diesel Quality
17(1)
3.6 Fuel Gas Heating Value
18(1)
3.7 Stock Level
19(3)
3.8 Batch Blending
22(1)
4 Characteristics of Data
23(28)
4.1 Data Types
23(1)
4.2 Memory
24(1)
4.3 Use of Historical Data
24(1)
4.4 Central Value
25(7)
4.5 Dispersion
32(1)
4.6 Mode
33(2)
4.7 Standard Deviation
35(2)
4.8 Skewness and Kurtosis
37(9)
4.9 Correlation
46(1)
4.10 Data Conditioning
47(4)
5 Probability Density Function
51(32)
5.1 Uniform Distribution
55(2)
5.2 Triangular Distribution
57(2)
5.3 Normal Distribution
59(3)
5.4 Bivariate Normal Distribution
62(3)
5.5 Central Limit Theorem
65(4)
5.6 Generating a Normal Distribution
69(1)
5.7 Quantile Function
70(1)
5.8 Location and Scale
71(2)
5.9 Mixture Distribution
73(1)
5.10 Combined Distribution
73(2)
5.11 Compound Distribution
75(1)
5.12 Generalised Distribution
75(1)
5.13 Inverse Distribution
76(1)
5.14 Transformed Distribution
76(1)
5.15 Truncated Distribution
77(1)
5.16 Rectified Distribution
78(1)
5.17 Noncentral Distribution
78(1)
5.18 Odds
79(1)
5.19 Entropy
80(3)
6 Presenting the Data
83(22)
6.1 Box and Whisker Diagram
83(1)
6.2 Histogram
84(6)
6.3 Kernel Density Estimation
90(5)
6.4 Circular Plots
95(2)
6.5 Parallel Coordinates
97(1)
6.6 Pie Chart
98(1)
6.7 Quantile Plot
98(7)
7 Sample Size
105(8)
7.1 Mean
105(1)
7.2 Standard Deviation
106(1)
7.3 Skewness and Kurtosis
107(1)
7.4 Dichotomous Data
108(2)
7.5 Bootstrapping
110(3)
8 Significance Testing
113(14)
8.1 Null Hypothesis
113(3)
8.2 Confidence Interval
116(2)
8.3 Six-Sigma
118(1)
8.4 Outliers
119(1)
8.5 Repeatability
120(1)
8.6 Reproducibility
121(1)
8.7 Accuracy
122(1)
8.8 Instrumentation Error
123(4)
9 Fitting a Distribution
127(20)
9.1 Accuracy of Mean and Standard Deviation
130(1)
9.2 Fitting a CDF
131(3)
9.3 Fitting a QF
134(1)
9.4 Fitting a PDF
135(3)
9.5 Fitting to a Histogram
138(3)
9.6 Choice of Penalty Function
141(6)
10 Distribution of Dependent Variables
147(18)
10.1 Addition and Subtraction
147(1)
10.2 Division and Multiplication
148(5)
10.3 Reciprocal
153(1)
10.4 Logarithmic and Exponential Functions
153(9)
10.5 Root Mean Square
162(2)
10.6 Trigonometric Functions
164(1)
11 Commonly Used Functions
165(20)
11.1 Euler's Number
165(1)
11.2 Euler-Mascheroni Constant
166(1)
11.3 Logit Function
166(1)
11.4 Logistic Function
167(1)
11.5 Gamma Function
168(6)
11.6 Beta Function
174(1)
11.7 Pochhammer Symbol
174(2)
11.8 Bessel Function
176(2)
11.9 Marcum Q-Function
178(2)
11.10 Riemann Zeta Function
180(1)
11.11 Harmonic Number
180(2)
11.12 Stirling Approximation
182(1)
11.13 Derivatives
183(2)
12 Selected Distributions
185(50)
12.1 Lognormal
186(3)
12.2 Burr
189(2)
12.3 Beta
191(4)
12.4 Hosking
195(9)
12.5 Student t
204(4)
12.6 Fisher
208(2)
12.7 Exponential
210(3)
12.8 Weibull
213(3)
12.9 Chi-Squared
216(5)
12.10 Gamma
221(4)
12.11 Binomial
225(6)
12.12 Poisson
231(4)
13 Extreme Value Analysis
235(10)
14 Hazard Function
245(8)
15 CUSUM
253(6)
16 Regression Analysis
259(32)
16.1 F Test
275(3)
16.2 Adjusted R2
278(1)
16.3 Akaike Information Criterion
279(2)
16.4 Artificial Neural Networks
281(5)
16.5 Performance Index
286(5)
17 Autocorrelation
291(8)
18 Data Reconciliation
299(6)
19 Fourier Transform
305(10)
Part 2: Catalogue of Distributions 315(254)
20 Normal Distribution
317(32)
20.1 Skew-Normal
317(3)
20.2 Gibrat
320(1)
20.3 Power Lognormal
320(1)
20.4 Logit-Normal
321(1)
20.5 Folded Normal
321(2)
20.6 Levy
323(2)
20.7 Inverse Gaussian
325(4)
20.8 Generalised Inverse Gaussian
329(1)
20.9 Normal Inverse Gaussian
330(2)
20.10 Reciprocal Inverse Gaussian
332(2)
20.11 Q-Gaussian
334(4)
20.12 Generalised Normal
338(7)
20.13 Exponentially Modified Gaussian
345(2)
20.14 Moyal
347(2)
21 Burr Distribution
349(12)
21.1 Type I
349(1)
21.2 Type II
349(1)
21.3 Type III
349(1)
21.4 Type IV
350(1)
21.5 Type V
351(1)
21.6 Type VI
351(2)
21.7 Type VII
353(1)
21.8 Type VIII
354(1)
21.9 Type IX
354(1)
21.10 Type X
355(1)
21.11 Type XI
356(1)
21.12 Type XII
356(1)
21.13 Inverse
357(4)
22 Logistic Distribution
361(16)
22.1 Logistic
361(3)
22.2 Half-Logistic
364(1)
22.3 Skew-Logistic
365(2)
22.4 Log-Logistic
367(2)
22.5 Paralogistic
369(1)
22.6 Inverse Paralogistic
370(1)
22.7 Generalised Logistic
371(4)
22.8 Generalised Log-Logistic
375(1)
22.9 Exponentiated Kumaraswamy-Dagum
376(1)
23 Pareto Distribution
377(12)
23.1 Pareto Type I
377(1)
23.2 Bounded Pareto Type I
378(1)
23.3 Pareto Type II
379(2)
23.4 Lomax
381(1)
23.5 Inverse Pareto
381(1)
23.6 Pareto Type III
382(1)
23.7 Pareto Type IV
383(1)
23.8 Generalised Pareto
383(2)
23.9 Pareto Principle
385(4)
24 Stoppa Distribution
389(4)
24.1 Type I
389(1)
24.2 Type II
389(2)
24.3 Type III
391(1)
24.4 Type IV
391(1)
24.5 Type V
392(1)
25 Beta Distribution
393(16)
25.1 Arcsine
393(1)
25.2 Wigner Semicircle
394(1)
25.3 Balding-Nichols
395(1)
25.4 Generalised Beta
396(1)
25.5 Beta Type II
396(3)
25.6 Generalised Beta Prime
399(1)
25.7 Beta Type IV
400(1)
25.8 PERT
401(2)
25.9 Beta Rectangular
403(1)
25.10 Kumaraswamy
404(3)
25.11 Noncentral Beta
407(2)
26 Johnson Distribution
409(6)
26.1 SN
409(1)
26.2 SU
410(2)
26.3 SL
412(1)
26.4 SB
412(1)
26.5 Summary
413(2)
27 Pearson Distribution
415(20)
27.1 Type I
416(1)
27.2 Type II
416(1)
27.3 Type III
417(1)
27.4 Type IV
418(6)
27.5 Type V
424(1)
27.6 Type VI
425(4)
27.7 Type VII
429(4)
27.8 Type VIII
433(1)
27.9 Type IX
433(1)
27.10 Type X
433(1)
27.11 Type XI
434(1)
27.12 Type XII
434(1)
28 Exponential Distribution
435(12)
28.1 Generalised Exponential
435(1)
28.2 Gompertz-Verhulst
435(1)
28.3 Hyperexponential
436(1)
28.4 Hypoexponential
437(1)
28.5 Double Exponential
438(1)
28.6 Inverse Exponential
439(1)
28.7 Maxwell-Juttner
439(1)
28.8 Stretched Exponential
440(1)
28.9 Exponential Logarithmic
441(1)
28.10 Logistic Exponential
442(1)
28.11 Q-Exponential
442(3)
28.12 Benktander
445(2)
29 Weibull Distribution
447(4)
29.1 Nukiyama-Tanasawa
447(1)
29.2 Q-Weibull
447(4)
30 Chi Distribution
451(12)
30.1 Half-Normal
451(1)
30.2 Rayleigh
452(2)
30.3 Inverse Rayleigh
454(1)
30.4 Maxwell
454(4)
30.5 Inverse Chi
458(1)
30.6 Inverse Chi-Squared
459(1)
30.7 Noncentral Chi-Squared
460(3)
31 Gamma Distribution
463(8)
31.1 Inverse Gamma
463(1)
31.2 Log-Gamma
463(4)
31.3 Generalised Gamma
467(1)
31.4 Q-Gamma
468(3)
32 Symmetrical Distributions
471(16)
32.1 Anglit
471(1)
32.2 Bates
472(1)
32.3 Irwin-Hall
473(2)
32.4 Hyperbolic Secant
475(1)
32.5 Arctangent
476(1)
32.6 Kappa
477(1)
32.7 Laplace
478(1)
32.8 Raised Cosine
479(2)
32.9 Cardioid
481(1)
32.10 Slash
481(2)
32.11 Tukey Lambda
483(3)
32.12 Von Mises
486(1)
33 Asymmetrical Distributions
487(38)
33.1 Benini
487(1)
33.2 Birnbaum-Saunders
488(2)
33.3 Bradford
490(1)
33.4 Champernowne
491(1)
33.5 Davis
492(2)
33.6 Frechet
494(2)
33.7 Gompertz
496(1)
33.8 Shifted Gompertz
497(1)
33.9 Gompertz-Makeham
498(1)
33.10 Gamma-Gompertz
499(1)
33.11 Hyperbolic
499(3)
33.12 Asymmetric Laplace
502(2)
33.13 Log-Laplace
504(2)
33.14 Lindley
506(1)
33.15 Lindley-Geometric
507(2)
33.16 Generalised Lindley
509(1)
33.17 Mielke
509(1)
33.18 Muth
510(2)
33.19 Nakagami
512(1)
33.20 Power
513(1)
33.21 Two-Sided Power
514(2)
33.22 Exponential Power
516(1)
33.23 Rician
517(1)
33.24 Topp-Leone
517(2)
33.25 Generalised Tukey Lambda
519(2)
33.26 Wakeby
521(4)
34 Amoroso Distribution
525(4)
35 Binomial Distribution
529(20)
35.1 Negative-Binomial
529(2)
35.2 Polya
531(1)
35.3 Geometric
531(4)
35.4 Beta-Geometric
535(1)
35.5 Yule-Simon
536(2)
35.6 Beta-Binomial
538(2)
35.7 Beta-Negative Binomial
540(1)
35.8 Beta-Pascal
541(1)
35.9 Gamma-Poisson
542(1)
35.10 Conway-Maxwell-Poisson
543(3)
35.11 Skellam
546(3)
36 Other Discrete Distributions
549(20)
36.1 Benford
549(3)
36.2 Borel-Tanner
552(3)
36.3 Consul
555(1)
36.4 Delaporte
556(2)
36.5 Flory-Schulz
558(1)
36.6 Hypergeometric
559(2)
36.7 Negative Hypergeometric
561(1)
36.8 Logarithmic
561(2)
36.9 Discrete Weibull
563(1)
36.10 Zeta
564(1)
36.11 Zipf
565(2)
36.12 Parabolic Fractal
567(2)
Appendix 1 Data Used in Examples 569(8)
Appendix 2 Summary of Distributions 577(14)
References 591(2)
Index 593
Myke King is Director of Whitehouse Consulting which provides process control consulting and training services. For the past 40 years he has been running courses for industry covering all aspects of process control, training over 2,000 students. He also lectures at several universities. He is author of the popular Process Control: A Practical Approach, now in its second edition (Wiley, 2016).