This function implements the monitoring phase (Phase II) of FRTM method.
FRTM_PhaseII(data_oc, mod_phaseI, ncores = 1)
A list containing the following arguments:
T2_fd
List of
SPE_fd
List of SPE functions for each observation.
CL_T2
Control limit of the Hotelling's
CL_SPE
Control limit of the SPE control chart.
x_err
A list containing the discrete observations for each curve.
grid_i
A list of vector of time points where the curves are sampled.
x_list_smooth
Smooth curves.
mod_phaseI
An object of class mod_phaseI_FRTM
obtained as output of the function FRTM_PhaseI
.
A list containing the following arguments: x_err
a list containing the discrete observations for each curve to be monitored; grid_i
a list of vector of time points where the curves to be monitored are sampled.
An object of class mod_phaseI_FRTM
obtained as output of the function FRTM_PhaseI
.
If ncores
>1, then parallel computing is used, with ncores
cores. Default is 1.
F. Centofanti
Centofanti, F., A. Lepore, M. Kulahci, and M. P. Spooner (2024). Real-time monitoring of functional data. Accepted for publication in Journal of Quality Technology.
library(funcharts)
data <- simulate_data_FRTM(n_obs = 20)
data_oc <-
simulate_data_FRTM(
n_obs = 2,
scenario = "1",
shift = "OC_h",
severity = 0.5
)
lambda <- 10 ^ -5
max_x <- max(unlist(data$grid_i))
seq_t_tot <- seq(0, 1, length.out = 30)[-1]
seq_x <- seq(0.1, max_x, length.out = 10)
if (FALSE) {
mod_phaseI_FRTM <- FRTM_PhaseI(
data_tra = data,
control.FDTW = list(
M = 30,
N = 30,
lambda = lambda,
seq_t = seq_t_tot,
iter_tem = 1,
iter = 1
),
control.rtr = list(seq_x = seq_x)
)
mod_phaseII_FRTM <- FRTM_PhaseII(data_oc = data_oc , mod_phaseI = mod_phaseI_FRTM)
plot(mod_phaseI_FRTM)
plot(mod_phaseII_FRTM)
}
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