ISO 7870-4:2011
Control charts — Part 4: Cumulative sum charts
ISO 7870-4:2011 provides statistical procedures for setting up cumulative sum (cusum) schemes for process and quality control using variables (measured) and attribute data. It describes general-purpose methods of decision-making using cumulative sum (cusum) techniques for monitoring, control and retrospective analysis.
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Scope
Normative references
Terms and definitions, abbreviated terms and symbols
Terms and definitions
Abbreviated terms
Symbols
Principal features of cumulative sum (cusum) charts
Basic steps in the construction of cusum charts — Graphical
Example of a cusum plot — Motor voltages
The process
Simple plot of results
Standard control chart for individual results
Cusum chart — Overall perspective
Cusum chart construction
Cusum chart interpretation
Introduction
The basics of interpretation of a cusum chart using “imagina
Interpretation using “actual” data
Manhattan diagram
Fundamentals of making cusum-based decisions
The need for decision rules
The basis for making decisions
Measuring the effectiveness of a decision rule
Basic concepts
Example of the calculation of ARL
Types of cusum decision schemes
Vmask types
Truncated Vmask
Configuration and dimensions
Application of the truncated Vmask
Average run lengths
General comments on average run lengths
Alternative design approaches
Semi-parabolic Vmask
Snub-nosed Vmask
Full Vmask
Fast initial response (FIR) cusum
Tabular cusum
Rationale
Tabular cusum method
Cusum methods for process and quality control
The nature of the changes to be detected
The size of change to be detected
“Step” changes
Drifting
Cyclic
Hunting
Selecting target values
General
Standard (given) value as target
Performancebased target
Cusum schemes for monitoring location
Standard schemes
Standard schemes — Limitations
“Tailored” cusum schemes
Cusum schemes for monitoring variation
General
Cusum schemes for subgroup ranges
Cusum schemes for subgroup standard deviations
Special situations
Large between-subgroup variation
“One-at-a-time” data
Serial dependence between observations
Outliers
Cusum schemes for discrete data
Event count — Poisson data
General
General cusum decision rules for discrete data
Cusum schemes for count data
Two classes data — binomial data
General
Situation 1: Tp \( 0,1 — Poisson-based
Situation 2: Tm \( 20 — Normal-based s
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Scope
Normative references
Terms and definitions, abbreviated terms and symbols
Terms and definitions
Abbreviated terms
Symbols
Principal features of cumulative sum (cusum) charts
Basic steps in the construction of cusum charts — Graphical
Example of a cusum plot — Motor voltages
The process
Simple plot of results
Standard control chart for individual results
Cusum chart — Overall perspective
Cusum chart construction
Cusum chart interpretation
Introduction
The basics of interpretation of a cusum chart using “imagina
Interpretation using “actual” data
Manhattan diagram
Fundamentals of making cusum-based decisions
The need for decision rules
The basis for making decisions
Measuring the effectiveness of a decision rule
Basic concepts
Example of the calculation of ARL
Types of cusum decision schemes
Vmask types
Truncated Vmask
Configuration and dimensions
Application of the truncated Vmask
Average run lengths
General comments on average run lengths
Alternative design approaches
Semi-parabolic Vmask
Snub-nosed Vmask
Full Vmask
Fast initial response (FIR) cusum
Tabular cusum
Rationale
Tabular cusum method
Cusum methods for process and quality control
The nature of the changes to be detected
The size of change to be detected
“Step” changes
Drifting
Cyclic
Hunting
Selecting target values
General
Standard (given) value as target
Performancebased target
Cusum schemes for monitoring location
Standard schemes
Standard schemes — Limitations
“Tailored” cusum schemes
Cusum schemes for monitoring variation
General
Cusum schemes for subgroup ranges
Cusum schemes for subgroup standard deviations
Special situations
Large between-subgroup variation
“One-at-a-time” data
Serial dependence between observations
Outliers
Cusum schemes for discrete data
Event count — Poisson data
General
General cusum decision rules for discrete data
Cusum schemes for count data
Two classes data — binomial data
General
Situation 1: Tp \( 0,1 — Poisson-based
Situation 2: Tm \( 20 — Normal-based s