rbcc {rbcc}R Documentation

Risk-based Statistical Control Charts

Description

Calculate Risk-based Shewhart type univarate Control Charts

Usage

rbcc (X, UC, C, n, type= c("xbar", "R", "S"), confidence_level=0.9973, K=3)

Arguments

X

vector of variable (numeric vector). Either can be simulated using data_gen or defined by using available data set.

UC

vector of measuerement error (numeric vector).Either can be simulated using data_gen or defined by using available previous information.

C

vector of decision costs (default value is vector of 1).

n

the sample size for grouping. For individual obervations use n=1).

type

a character string specifying the type of Shewhart control chart. Available types are; "Xbar", "R"and "S".

confidence_level

the (1-alpha)percent confidence level (default value is 0.99)

K

a correction component (default value is 3)

Value

cost0

Total cost of a monitoring process

cost1

Total cost of correct acceptance related to a process monitoring

cost2

Total cost of decision error type 1 related to a process monitoring

cost3

Total cost of decision error type 2 related to a process monitoring

cost4

Total cost of correct reject related to a process monitoring

LCLx

Lower control limit of a Shewhart univariate 'type' chart for a given data

UCLx

Upper control limit of a Shewhart univariate 'type' chart for a given data

LCLy

Lower control limit of a Shewhart univariate 'type' chart for a given data with measurement uncertainity

UCLy

Upper control limit of a Shewhart univariate 'type' chart for a given data with measurement uncertainity

real

Real values of a Shewhart univariate 'type' chart statistic

Observed

Observed values of a Shewhart univariate 'type' chart with measurement errors

Author(s)

Aamir Saghir, Attila I. Katona, Zsolt T. Kosztyan*

e-mail: kzst@gtk.uni-pannon.hu

References

Katona, A. I., Saghir, A., Hegedűs, C., & Kosztyán, Z. T. (2023). Design of Risk-Based Univariate Control Charts with Measurement Uncertainty. IEEE Access.

Kosztyán, Z. T., & Katona, A. I. (2016). Risk-based multivariate control chart. Expert Systems with Applications, 62, 250-262.

See Also

data_gen, rbcc_opt, rbcusumcc, rbcusumcc_opt,rbewmacc, rbewmacc_opt, rbmacc, rbmacc_opt, rbmcc, rbmcc_opt, plot.rbcc, summary.rbcc.

Examples


# Data Generation and Xbar chart.

## Example for generation of data vector X and measuremenet error vector UC.
obs <- 200                 # Total number of observations of a process.
mu_X <- c(0)               # Define data mean.
va_X  <- c(1)              # Define data standard deviation.
sk_X <- c(0)               # Define data skewness.
ku_X <- c(3)               # Define data kurtosis.
mu_UC <- c(0)              # Define mean of measurement errors.
va_UC <- c(1)              # Define standard deviation of measurement errors.
sk_UC <- c(0)              # Define skewness of measurement errors.
ku_UC <- c(3)              # Define kurtosis of measurement errors.

# Simulation of 200 obervations of 1 variable.
X <- data_gen (obs, mu_X, va_X, sk_X, ku_X)

# Simulation of 200 muasurement erros related to 1 variable.
UC <- data_gen(obs,mu_UC, va_UC, sk_UC, ku_UC)

# Construction of risk-based Xbar chart with default vector of decision costs
C <- c(1,1,1,1)                         # vector of decision costs
H <- rbcc(X, UC, C, n=3, type="xbar")   # for subgroups of size 3
plot(H)                                 # plot RBCC

# optimal risk-based xbar control chart
H_opt <- rbcc_opt(X, UC, C, n=3, type="xbar")
print(H_opt) 
summary(H_opt) 

[Package rbcc version 0.1.5 Index]