ICISE Palermo 2016 presentation Tsiamyrtzis .pdf
File information
Original filename: ICISE_Palermo_2016_presentation_Tsiamyrtzis.pdf
Title: ICISE-023: A Bayesian Approach for Online Monitoring of Phase I Data
Author: Konstantinos Bourazas, Dimitris Kiagias, Panagiotis Tsiamyrtzis
This PDF 1.4 document has been generated by LaTeX with beamer class version 3.07 / pdfTeX-1.40.10, and has been sent on pdf-archive.com on 25/07/2017 at 08:50, from IP address 109.242.x.x.
The current document download page has been viewed 293 times.
File size: 674 KB (52 pages).
Privacy: public file
Share on social networks
Link to this file download page
Document preview
ICISE-023: A Bayesian Approach for
Online Monitoring of Phase I Data
Konstantinos Bourazas1
1 Dept.
Dimitris Kiagias2
Panagiotis Tsiamyrtzis1
of Statistics, Athens University of Economics and Business, Greece
kbourazas@aueb.gr, pt@aueb.gr
2 School
of Mathematics and Statistics, University of Sheffield, UK
kiagias.dim@gmail.com
Palermo, 20 June 2016
Bourazas, Kiagias, Tsiamyrtzis (AUEB-US)
ICISE-023
Palermo, 20 June 2016
1 / 17
Introduction
In Statistical Process Control/Monitoring (SPC/M) our goal is to
identify as soon as possible when a process moves from the In Control
(IC) to the Out of Control (OOC) state, while we keep the false alarm
rate at a very low (predetermined) level.
Bourazas, Kiagias, Tsiamyrtzis (AUEB-US)
ICISE-023
Palermo, 20 June 2016
2 / 17
Introduction
In Statistical Process Control/Monitoring (SPC/M) our goal is to
identify as soon as possible when a process moves from the In Control
(IC) to the Out of Control (OOC) state, while we keep the false alarm
rate at a very low (predetermined) level.
In frequentist based SPC/M the parameter(s) of interest θ is
considered to be an unknown constant and typically the goal is to
identify transient or persistent shifts of the unknown parameter(s).
Bourazas, Kiagias, Tsiamyrtzis (AUEB-US)
ICISE-023
Palermo, 20 June 2016
2 / 17
Introduction
In Statistical Process Control/Monitoring (SPC/M) our goal is to
identify as soon as possible when a process moves from the In Control
(IC) to the Out of Control (OOC) state, while we keep the false alarm
rate at a very low (predetermined) level.
In frequentist based SPC/M the parameter(s) of interest θ is
considered to be an unknown constant and typically the goal is to
identify transient or persistent shifts of the unknown parameter(s).
Parametric SPC/M control chart methods, like Shewhart control
charts, CUSUM and EWMA, will require the knowledge of the in
control distribution process parameter(s). In practice, this is handled
with the employment of an offline calibration (phase I) period, prior
to the online control/monitoring of the process (phase II).
Bourazas, Kiagias, Tsiamyrtzis (AUEB-US)
ICISE-023
Palermo, 20 June 2016
2 / 17
Introduction
In this talk we will focus in how we can perform online monitoring
during phase I, with emphasis in identifying outliers and propose a
self-starting control/monitoring scheme which will be free from the
phase I requirement.
Bourazas, Kiagias, Tsiamyrtzis (AUEB-US)
ICISE-023
Palermo, 20 June 2016
3 / 17
Introduction
In this talk we will focus in how we can perform online monitoring
during phase I, with emphasis in identifying outliers and propose a
self-starting control/monitoring scheme which will be free from the
phase I requirement.
Phase I (and short run) analysis in frequentist based SPC/M assumes
that we have iid data from the In Control distribution. These data are
used (retrospectively) to estimate the unknown parameter(s) while
testing is performed offline. In case of alarms, the standard iterative
approach is to remove the alarms and recalculate the control limits,
until we get no alarms.
Bourazas, Kiagias, Tsiamyrtzis (AUEB-US)
ICISE-023
Palermo, 20 June 2016
3 / 17
Introduction
In this talk we will focus in how we can perform online monitoring
during phase I, with emphasis in identifying outliers and propose a
self-starting control/monitoring scheme which will be free from the
phase I requirement.
Phase I (and short run) analysis in frequentist based SPC/M assumes
that we have iid data from the In Control distribution. These data are
used (retrospectively) to estimate the unknown parameter(s) while
testing is performed offline. In case of alarms, the standard iterative
approach is to remove the alarms and recalculate the control limits,
until we get no alarms.
The above procedure is known to have certain deficiencies:
Bourazas, Kiagias, Tsiamyrtzis (AUEB-US)
ICISE-023
Palermo, 20 June 2016
3 / 17
Issues in frequentist phase I analysis
Phase I assumes iid data from the in control distribution. What if the
parameter shifts during phase I?
Bourazas, Kiagias, Tsiamyrtzis (AUEB-US)
ICISE-023
Palermo, 20 June 2016
4 / 17
Issues in frequentist phase I analysis
Phase I assumes iid data from the in control distribution. What if the
parameter shifts during phase I?
Phase I needs to be long enough to provide reliable estimates. What
if we have short runs?
Bourazas, Kiagias, Tsiamyrtzis (AUEB-US)
ICISE-023
Palermo, 20 June 2016
4 / 17
Link to this page
Permanent link
Use the permanent link to the download page to share your document on Facebook, Twitter, LinkedIn, or directly with a contact by e-Mail, Messenger, Whatsapp, Line..
Short link
Use the short link to share your document on Twitter or by text message (SMS)
HTML Code
Copy the following HTML code to share your document on a Website or Blog