Quality and Reliability Methods - SAS

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Contents Cumulative Sum (Cusum) Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Launch Options for Cusum Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Cusum Chart Options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Formulas for CUSUM Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .104 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .104 One-Sided CUSUM Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .104 Two-Sided Cusum Schemes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .106
Chapter 6 Cumulative Sum Control Charts 97 Cumulative Sum (Cusum) Charts Cumulative Sum (Cusum) Charts Cumulative Sum (Cusum) charts display cumulative sums of subgroup or individual measurements from a target value. Cusum charts are graphical and analytical tools for deciding whether a process is in a state of statistical control and for detecting a shift in the process mean. JMP cusum charts can be one-sided, which detect a shift in one direction from a specified target mean, or two-sided to detect a shift in either direction. Both charts can be specified in terms of geometric parameters (h and k shown in Figure 6.2); two-sided charts allow specification in terms of error probabilities α and β. To interpret a two-sided Cusum chart, you compare the points with limits that compose a V-mask. A V-mask is formed by plotting V-shaped limits. The origin of a V-mask is the most recently plotted point, and the arms extended backward on the x-axis, as in Figure 6.2. As data are collected, the cumulative sum sequence is updated and the origin is relocated at the newest point. Figure 6.2 Illustration of a V-Mask for a Two-Sided Cusum Chart upper arm vertex lower arm d h, the rise in the arm corresponding to the distance (d) from origin to vertex 1 unit k, the rise in the arm corresponding to one sampling unit Shifts in the process mean are visually easy to detect on a cusum chart because they produce a change in the slope of the plotted points. The point where the slope changes is the point where the shift occurs. A condition is out-of-control if one or more of the points previously plotted crosses the upper or lower arm of the V-mask. Points crossing the lower arm signal an increasing process mean, and points crossing the upper arm signal a downward shift. There are major differences between cusum charts and other control (Shewhart) charts: • A Shewhart control chart plots points based on information from a single subgroup sample. In cusum charts, each point is based on information from all samples taken up to and including the current subgroup.
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Version 10 Quality and Reliability
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Nelson Rules . . . . . . . . . .
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8 8 Assess Measurement Systems The
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10 Other Menu Options . . . . . . .
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12 Reliability Forecast Platform Op
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Chapter 1 Learn about JMP Documenta
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Chapter 1 Learn about JMP 17 Book C
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Chapter 1 Learn about JMP 19 Book C
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Chapter 1 Learn about JMP 21 Additi
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Chapter 1 Learn about JMP 23 Additi
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Chapter 2 Statistical Control Chart
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Page 45 and 46: Chapter 2 Statistical Control Chart
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Page 59 and 60: Chapter 4 Interactive Control Chart
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Chapter 9 Capability Analyses 147 C
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Chapter 9 Capability Analyses 149 C
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Chapter 10 Variability Charts Varia
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Chapter 10 Variability Charts 153 V
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Chapter 10 Variability Charts 163 R
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Chapter 10 Variability Charts 169 D
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Chapter 10 Variability Charts 171 A
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Chapter 11 Pareto Plots The Pareto
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Chapter 11 Pareto Plots 181 Pareto
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Chapter 11 Pareto Plots 187 Launch
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Chapter 11 Pareto Plots 189 One-Way
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Chapter 11 Pareto Plots 191 Two-Way
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Chapter 11 Pareto Plots 193 Defect
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Chapter 11 Pareto Plots 195 Defect
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Chapter 12 Ishikawa Diagrams The Di
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Chapter 12 Ishikawa Diagrams 199 Pr
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Chapter 12 Ishikawa Diagrams 201 Bu
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Chapter 13 Lifetime Distribution Us
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Chapter 13 Lifetime Distribution 20
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Chapter 14 Lifetime Distribution II
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Chapter 15 Recurrence Analysis The
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Chapter 15 Recurrence Analysis 263
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Chapter 16 Degradation Using the De
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Chapter 16 Degradation 279 Overview
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Chapter 16 Degradation 281 The Degr
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Chapter 16 Degradation 283 Model Sp
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Chapter 16 Degradation 291 Inverse
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Chapter 16 Degradation 293 Platform
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Chapter 16 Degradation 295 Platform
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Chapter 16 Degradation 297 Model Re
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Chapter 16 Degradation 299 Destruct
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Chapter 16 Degradation 301 Stabilit
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Chapter 17 Forecasting Product Reli
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Chapter 18 Reliability Growth Using
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Chapter 18 Reliability Growth 319 O
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Chapter 18 Reliability Growth 321 E
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Chapter 18 Reliability Growth 327 T
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Chapter 18 Reliability Growth 329 F
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Chapter 18 Reliability Growth 343 A
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Chapter 18 Reliability Growth 345 A
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Chapter 18 Reliability Growth 347 S
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Chapter 19 Reliability and Survival
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References Abernethy, Robert B. (19
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References 407 Carroll, R.J., Ruppe
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References 409 Haaland, P.D. (1989)
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References 411 Lawless, J.F. (1982)
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References 413 Nelson, W.B. (2003),
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References 415 Snee, R.D. and Marqu
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Index JMP Quality and Reliability M
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Index 419 Fit SEV 242 Fit Weibull 2
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Index 421 Q Quantiles table 356 R R
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Index 423 Weibull Plot 360 Westgard
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Quality and Reliability Methods - SAS

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