Details

Statistics for Process Control Engineers


Statistics for Process Control Engineers

A Practical Approach
1. Aufl.

von: Myke King

127,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 10.08.2017
ISBN/EAN: 9781119383529
Sprache: englisch
Anzahl Seiten: 624

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Beschreibungen

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

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