Chapter 1. Introduction Chapter One Introductionise.tamu.edu/inen614/Chapter 1.pdf · Chapter One Introduction Chapter 1. Introduction ... 13, 1994) that contains eight ... - robust

ISEN 614 Advanced Quality Control (Anomaly and Change Detection) Spring 2008 Dr. Yu Ding

1

Chapter One Introduction

Chapter 1. Introduction

- About the course

- Motivations and examples

- A brief history and course organization

- Relation to other courses

- Some basic concepts


2

Course syllabus

• ISEN 614 Advanced Quality Control

(Anomaly and Change Detection)

• Instructor: Dr. Yu Ding

• Course website: http://ise.tamu.edu/inen614

• Syllabus

• Course project

• Academic IntegrityAggie Honor Code: “An Aggie does not lie, cheat,

or steal or tolerate those who do.”


3

What is the course about?

• Fundamentally, this course is about concepts and

methodologies of change and anomaly detection.

• People usually try to detect “abnormal” from

“normal”.

• Examples of “abnormal” or anomalies: in quality

control, a bad product; in security applications,

criminals and terrorists; in healthcare application,

medical errors and disease outbreaks.

• In a process or environment, the detection of

existence of “abnormal” is anomaly detection; the

detection of the moment where “abnormal” appears

is change detection.


4

Example 1.1: A univariate process in quality control (QC)

• Example 1.1: Frozen orange juice concentrate is

packed in 6-oz cardboard cans. These cans are

formed on a machine by spinning them from

cardboard stock and attaching a metal bottom panel.

As part of the QC process, people need to inspect

possible leak either on the side seam or around the

bottom joint. There are 30 samples, each of which

has 50 cans were selected at half-hour intervals over

a three-shift period.


5

Example 1.1 (continued)

• Data table


6


• Data and chart

• Question: Is the fluctuation an unavoidable part of the underlying

process or is it an indication of some kind of process change?

0 5 10 15 20 25 30

4

6

8

10

12

14

16

18

20

22

24n

um

ber

of

no

nco

nfo

rmin

g

can

s in

each

sam

ple

sample index


7

Example 1.2 Monitoring medical errors

• Example 1.2: In a hospital, there are three major types of

medical errors to be detected and avoided. Every day, a

medical safety personnel will inspect a sample of 30 cases

and record the errors happened in each category.

• The person will send out an alert if s/he believes the error

rate increases out of the expected range and that it is likely

caused by a systematic root cause.

• However, an alert should be triggered only when it is

justifiable or “significant” in some sense. Otherwise, too

many alerts will practically shut down the hospital.


8


• Data table (multivariate)

• Question: when to send out alerts and which errors are the main

contributors?


9

Example 1.3 Scanning for unusual clusters

• Example 1.3: Over a five year period, 1991 to 1995, there

were 19 cases of a particular type of cancer reported in a

city. In reviewing the data, the epidemiologist notes that

there is a 1 year period (from April 4, 1993 through April

13, 1994) that contains eight cases.

• Question: Given 19 cases over 5 years, how unusual is it to

have a 1 year period containing as many as eight cases?


10

Example 1.4 Early warning system for West Nile virus

• Example 1.4: Since 1999 West Nile virus (WNV) outbreak in

New York City, which caused thousands of human infection

and 59 severe cases including 7 deaths, health officials have

been searching for an early warning system that could have

prevent human illness and death.

• In the summer of 2001, the New York City Department of

Health and Mental Hygiene established a citywide network of

adult mosquito traps, sentinel bird flocks, and system for

reporting, collecting, and testing dead birds.

• Health officials try to use the collected data and the pattern

embedded therein to set off public health alerts enough time

before onset of human cases.


11

Example 1.4 Early warning system for West Nile virus

• Question: Whether and when to

send out the alert? And where

is the potential outbreak epi-

center(s)?


12

Change and anomaly detection

• The objective of change and anomaly detection is to answer

the question: How “strange” is everything that has

happened in the last hours/days/months, given the historical

and recent observations?

• The answer to that question helps determine a proper

subsequent action, including, for example, active and more

intense data collections; stop a production process; alter the

public; dispatch interdiction force (police and other law

enforcements) etc.


13

A brief history

• Two major branches of development:

- In the applications of public-health surveillance

- In the applications of industrial quality control

• Many tools are shared between these two areas but an

essential difference is that the public-health surveillance

applications deal primarily with discrete data, while the

industrial QC deal by and large with the continuous data.

• A recent new branch of relevant research is in computer

science because of the database applications and data-

mining demands.

- A Carnegie Mellon University group: Andrew Moore

and WSARE (What’s Strange About Recent Event)


14

Course content

• Chapter 1 Introduction

- Motivation and examples

- History and course organization

- Relation to other courses

• Chapter 2 Univariate detection- Shewhart method, CUSUM, EWMA

- Discrete data

- Risk adjustment

• Chapter 3 Multivariate detection- T2 statistics, m-CUSUM, m-EWMA

- Data reduction and profile signal analysis

- Multivariate discrete data

• Chapter 4 Spatial-temporal scan statistics

- Scan statistics

- One-dimensional time analysis

- High-dimensional spatial or time-space analysis


15

Relation to other QC-relevant courses

Quality Management and Engineering

Design Stage

(product/process)

Real-time

In-Process

Final product or

check points

- robust design;

- Taguchi method

- design of experiments

ISEN 414

ISEN 616

- anomaly monitoring

and prediction

- change detection

ISEN 614

ISEN 619

- SPC control charts

- univariate, independent

process

ISEN 314

Mathematical and Statistical Foundation

… STAT 211/212ISEN 314 STAT

211/212


16

Relation to ISEN 619

• ISEN 619: Analysis and Prediction

• ISEN 619 focuses on predictive modeling. Given a set of data {xi, yi},

i = 1, …, N, xi is the input and yi is the corresponding response, can we

establish a relationship between xi and yi so as to allow us to predict the

value of y at a future x or an unmeasured x?

• ISEN 614 is different – it focuses much less on prediction modeling but

more on detection occurred or ongoing unusual events. Given a set of

data {xi}, i =1,…,N, does there exist an unusual pattern in the data

(which indicates some strange events or foul plays), and if so, is that

event still ongoing?

• The predictive model established in 619 can be used to build some type

of baseline for comparing what is “unusual” or “anomalous.” On the

other hand, the detection by 614 can help identify the anomalous events

to be used to train a prediction model.


17

Some basic concepts

• Performance measure:

- False alarm (FA): when a detection method indicates

an anomaly but in actuality it is not.

- Miss detection: when a detection method deems an

event normal but it turns out to be an anomaly.

- Detection power (DP) = 1 – miss detection


18

Some basic concepts

• Retrospective versus prospective analysis:

- Retrospective analysis is to study a set of historical data to see

if there is any unusual pattern in it.

- Prospective analysis is to study a set of historical AND ongoing

data to see if an unusual a pattern is emerging and likely to

continue on into the future.

• They are also called Phased I (retrospective) and Phase II

(prospective) analysis. Sometimes, also called “off-line” versus

“in-line” analysis.

• The methodology used in both analyses bear a great similarity.

Unless otherwise indicated, a technique can be used for both

purposes.

• Note that prospective analysis is not the prediction in ISEN 619’s

definition. The prospective analysis here is considered rather

retrospective in a prediction modeling approach.

Documents

Chapter 1. Introduction Chapter One Introductionise.tamu.edu/inen614/Chapter 1.pdf · Chapter One Introduction Chapter 1. Introduction ... 13, 1994) that contains eight ... - robust