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Produktbild: Statistical Control by Monitoring and Adjustment

Statistical Control by Monitoring and Adjustment

Aus der Reihe Wiley Series in Probability and Statistics

Fr. 189.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.04.2009

Verlag

John Wiley & Sons

Seitenzahl

360

Maße (L/B/H)

23.1/15.5/2 cm

Gewicht

499 g

Auflage

2nd edition

Sprache

Englisch

ISBN

978-0-470-14832-7

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.04.2009

Verlag

John Wiley & Sons

Seitenzahl

360

Maße (L/B/H)

23.1/15.5/2 cm

Gewicht

499 g

Auflage

2nd edition

Sprache

Englisch

ISBN

978-0-470-14832-7

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Statistical Control by Monitoring and Adjustment

  • Preface xi

    1 Introduction and Revision of Some Statistical Ideas 1

    1.1 Necessity for Process Control 1

    1.2 SPC and EPC 1

    1.3 Process Monitoring Without a Model 3

    1.4 Detecting a Signal in Noise 4

    1.5 Measurement Data 4

    1.6 Two Important Characteristics of a Probability Distribution 5

    1.7 Normal Distribution 6

    1.8 Normal Distribution Defined by ¿ and ¿ 6

    1.9 Probabilities Associated with Normal Distribution 7

    1.10 Estimating Mean and Standard Deviation from Data 8

    1.11 Combining Estimates of ¿2 9

    1.12 Data on Frequencies (Events): Poisson Distribution 10

    1.13 Normal Approximation to Poisson Distribution 12

    1.14 Data on Proportion Defective: Binomial Distribution 12

    1.15 Normal Approximation to Binomial Distribution 14

    Appendix 1A: Central Limit Effect 15

    Problems 17

    2 Standard Control Charts Under Ideal Conditions As a First Approximation 21

    2.1 Control Charts for Process Monitoring 21

    2.2 Control Chart for Measurement (Variables) Data 22

    2.3 Shewhart Charts for Sample Average and Range 24

    2.4 Shewhart Chart for Sample Range 26

    2.5 Process Monitoring With Control Charts for Frequencies 29

    2.6 Data on Frequencies (Counts): Poisson Distribution 30

    2.7 Common Causes and Special Causes 34

    2.8 For What Kinds of Data Has the c Chart Been Used? 36

    2.9 Quality Control Charts for Proportions: p Chart 37

    2.10 EWMA Chart 40

    2.11 Process Monitoring Using Cumulative Sums 46

    2.12 Specification Limits, Target Accuracy, and Process Capability 53

    2.13 How Successful Process Monitoring can Improve Quality 56

    Problems 57

    3 What Can Go Wrong and What Can We Do About It? 61

    3.1 Introduction 61

    3.2 Measurement Charts 64

    3.3 Need for Time Series Models 65

    3.4 Types of Variation 65

    3.5 Nonstationary Noise 66

    3.6 Values for constants 71

    3.7 Frequencies and Proportions 74

    3.8 Illustration 76

    3.9 Robustness of EWMA 78

    Appendix 3A: Alternative Forms of Relationships for EWMAs 79

    Questions 79

    4 Introduction to Forecasting and Process Dynamics 81

    4.1 Forecasting with an EWMA 81

    4.2 Forecasting Sales of Dingles 82

    4.3 Pete's Rule 85

    4.4 Effect of Changing Discount Factor 86

    4.5 Estimating Best Discount Factor 87

    4.6 Standard Deviation of Forecast Errors and Probability Limits for Forecasts 88

    4.7 What to Do If You Do Not Have Enough Data to Estimate ¿ 89

    4.8 Introduction to Process Dynamics and Transfer Function 89

    4.9 Dynamic Systems and Transfer Funtions 90

    4.10 Difference Equations to Represent Dynamic Relations 90

    4.11 Representing Dynamics of Industrial Process 96

    4.12 Transfer Function Models Using Difference Equations 97

    4.13 Stable and Unstable Systems 98

    Problems 100

    5 Nonstationary Time Series Models for Process Disturbances 103

    5.1 Reprise 103

    5.2 Stationary Time Series Model in Which Successive Values are Correlated 104

    5.3 Major Effects of Statistical Dependence: Illustration 105

    5.4 Random Walk 106

    5.5 How to Test a Forecasting Method 107

    5.6 Qualification of EWMA as a Forecast 107

    5.7 Understanding Time Series Behavior with Variogram 110

    5.8 Sticky Innovation Generating Model for Nonstationary Noise 113

    5.9 Robustness of EWMA for Signal Extraction 118

    5.10 Signal Extraction for Disturbance Model Due to Barnard 118

    Questions 122

    Problems 122

    6 Repeated-Feedback Adjustment 125

    6.1 Introduction to Discrete-Feedback Control 125

    6.2 Inadequacy of NIID Models and Other Stationary Models for Control: Reiteration 125

    6.3 Three Approaches to Repeated-Feedback Adjustment that Lead to Identical Conclusions 126

    6.4 Some History 130

    6.5 Adjustment Chart 132

    6.6 Insensitivity to Choice of G 134

    6.7 Compromise Value for G 135

    6.8 Using Smaller Value of G to Reduce Adjustment Variance ¿2x 136

    Appendix 6A: Robustness of Integral Control 137

    Appendix 6B: Effect on Adjustment of Choosing G Different from ¿0: Obtaining Equation (6.12) 139

    Appendix 6C: Average Reduction in Mean-Square Error Due to Adjustment for Observations Generated by IMA Model 140

    Questions 140

    Problems 140

    7 Periodic Adjustment 143

    7.1 Introduction 143

    7.2 Periodic Adjustment 143

    7.3 Starting Scheme for Periodic Adjustment 146

    7.4 Numerical Calculations for Bounded Adjustment 146

    7.5 Simple Device for Facilitating Bounded Adjustment 150

    7.6 Bounded Adjustment Seen as Process of Tracking 153

    7.7 Combination of Adjustment and Monitoring 153

    7.8 Bounded Adjustment for Series not Generated by IMA Model 155

    Problems 160

    8 Control of Process with Inertia 163

    8.1 Adjustment Depending on Last Two Output Errors 163

    8.2 Minimum Mean-Square Error Control of Process With First-Order Dynamics 167

    8.3 Schemes with Constrained Adjustment 169

    8.4 PI Schemes with Constrained Adjustment 170

    8.5 Optimal and Near-Optimal Constrained PI Schemes: Choice of P 171

    8.6 Choice of G For P = 0 and P = ¿0.25 172

    8.7 PI Schemes for Process With Dead Time 178

    8.8 Process Monitoring and Process Adjustment 181

    8.9 Feedback Adjustment Applied to Process in Perfect State of Control 182

    8.10 Using Shewhart Chart to Adjust Unstable Process 182

    8.11 Feedforward Control 183

    Appendix 8A: Equivalence of Equations for PI Control 184

    Appendix 8B: Effect of Errors in Adjustment 184

    Appendix 8C: Choices for G and P to Attain Optimal Constrained PI Control for Various Values of ¿0 and ¿0 with d0 = 0 and d0 = 1 185

    Questions 191

    Problems 191

    9 Explicit Consideration of Monetary Cost 193

    9.1 Introduction 193

    9.2 How Often Should You Take Data? 197

    9.3 Choosing Adjustment Schemes Directly in Terms of Costs 203

    Appendix 9A: Functions h(L/¿¿a) and q(L/¿¿a) in Table 9.1 205

    Appendix 9B: Calculation of Minimum-Cost Schemes 205

    Problems 207

    10 Cuscore Charts: Looking for Signals in Noise 209

    10.1 Introduction 209

    10.2 How Are Cuscore Statistics Obtained? 216

    10.3 Efficient Monitoring Charts 219

    10.4 Useful Method for Obtaining Detector When Looking for Signal in Noise Not Necessarily White Noise 221

    10.5 Looking for Single Spike 223

    10.6 Some Time Series Examples 224

    Appendix 10A: Likelihood, Fisher's Efficient Score, and Cuscore Statistics 227

    Appendix 10B: Useful Procedure for Obtaining Appropriate Cuscore Statistic 230

    Appendix 10C: Detector Series for IMA Model 231

    Problems 231

    11 Monitoring an Operating Feedback System 235

    11.1 Looking for Spike in Disturbance zt Subjected to Integral Control 235

    11.2 Looking for Exponential Signal in Disturbance Subject to Integral Control 237

    11.3 Monitoring Process with Inertia Represented by First-Order Dynamics 238

    11.4 Reconstructing Disturbance Pattern 240

    Appendix 11A: Derivation of Equation (11.3) 240

    Appendix 11B: Derivation of Equation (11.10) 242

    Appendix 11C: Derivation of Equation (11.14) 243

    12 Brief Review of Time Series Analysis 245

    12.1 Serial Dependence: Autocorrelation Function and Variogram 245

    12.2 Relation of Autocorrelation Function and Variogram 246

    12.3 Some Time Series Models 247

    12.4 Stationary Models 247

    12.5 Autoregressive Moving-Average Models 250

    12.6 Nonstationary Models 253

    12.7 IMA [or ARIMA(0, 1, 1)] Model 253

    12.8 Modeling Time Series Data 255

    12.9 Model Identification, Model Fitting, and Diagnostic Checking 256

    12.10 Forecasting 261

    12.11 Estimation with Closed-Loop Data 266

    12.12 Conclusion 269

    Appendix 12A: Other Tools for Identification of Time Series Models 269

    Appendix 12B: Estimation of Time Series Parameters 270

    Solutions to Exercises and Problems 273

    References and Further Reading 307

    Appendix Three Time Series 321

    Index 327