quality recognition & prediction: smarter pattern technology with the mahalanobis-taguchi system

CONTENTS

FOREWORD xiiiPREFACE xvACKNOWLEDGMENTS xvii

1 PATTERN RECOGNITION AND THE MT SYSTEM 1

1.1 Overview of Pattern Recognition and the Fields of Application 11.1.1 Features of Human Pattern Recognition 11.1.2 Characteristics of Pattern Recognition Performed by Machines 21.1.3 Fields that Use Pattern Recognition Applications 3

1.2 Standard Execution Procedure for Pattern Recognition 41.2.1 Defi nition of Purpose 41.2.2 Defi nition of the Standard State 41.2.3 Defi nition of the Measurement Items 41.2.4 Preprocessing 51.2.5 Feature Extraction 71.2.6 Creation of a Recognition Function 71.2.7 Recognition Processing and Judgment Processing 71.2.8 Cause Diagnostics 7

1.3 Fields with Substantial Experience in the Use of MT System Applications 81.3.1 Product Characteristics Inspection 81.3.2 Monitoring of Production Process and Equipment 121.3.3 Medical Applications 121.3.4 Economic Worth Estimation 13

2 MERITS OF THE MT SYSTEM AND ITS COMPUTATION METHODS 15

2.1 Characteristics Shared by all MT System Components 152.1.1 The “Unit Space” Concept 152.1.2 Examples of Unit Space 17

vii

viii • CONTENTS

2.1.3 Item Selection and Cause Diagnostics 192.1.4 MT System Architecture 23

2.2 Features of the MT Method 262.2.1 Overview 262.2.2 Example of Amount of Rainfall and Number of Umbrellas Sold 272.2.3 Computation of the Mahalanobis Distance 292.2.4 Supplementary Notes 32

2.3 Features of the T Method 332.3.1 Features of the T Method-1 332.3.2 Features of the RT Method (= T Method-3) 34

2.4 The MT System Computation Formulas 372.4.1 Computation Formula for the MT Method 372.4.2 Computation Formula for the T Method-1 402.4.3 Computation Formula for the RT Method (T Method-3) 46

3 DATA HANDLED BY THE MT SYSTEM AND FEATURE EXTRACTION 55

3.1 Use of Measured Values in an Unmodifi ed Form 553.2 Performing Feature Extraction 553.3 Feature Extraction Technique from Character Pattern 573.4 Feature Extraction Technique from Waveform Pattern 58

3.4.1 Defi nitions of Variation and Abundance as they Relate to Waveform Patterns 59

3.4.2 Meaning of Variation Value and Abundance Value in a Waveform Pattern 61

3.5 Differences Between Other Waveform Features and Variation Values/Abundance Values 623.5.1 Frequency Analysis 623.5.2 Wavelet 633.5.3 How Frequency Analysis and Wavelets Differ from

Variation and Abundance Values 63

4 MT METHOD APPLICATION PROCEDURE AND IMPORTANT POINTS TO HEED 65

4.1 Example of Character Recognition 654.1.1 Defi nition of a Unit Space 654.1.2 Feature Extraction 664.1.3 Computation of the Mahalanobis Distance of

Feature Space and Unit Data 674.1.4 Computation of the Mahalanobis Distance of

Unknown (Target) Data 72

CONTENTS • ix

4.1.5 Cause Diagnostics 744.1.6 Setting a Threshold 754.1.7 Unit Space Renewal 79

4.2 Example of Weather Prediction 804.2.1 Concepts Underlying Weather Prediction 804.2.2 Meteorological Data and Preprocessing 824.2.3 Defi nition of Unit Space 834.2.4 Rainfall Prediction Based on Target Data 83

5 T METHOD APPLICATION PROCEDURES AND KEY POINTS 87

5.1 Yield Prediction for Manufacturing-Production Using T Method-1 875.1.1 Defi nition of the Unit Space 885.1.2 Defi nition of Signal Data 895.1.3 Normalization of Signal Data 895.1.4 Computation of Proportional Coeffi cient and

SN Ratio of Each Item of Signal Data 905.1.5 Computation of Signal Data Integrated Estimate Value 945.1.6 Computation of the SN Ratio (db) for Integrated Estimate Value 955.1.7 Evaluation of the Importance of an Item 965.1.8 Integrated Yield Estimation for Unknown Data 985.1.9 Computation of Integrated Estimate Values

Before Normalization 995.2 Character Pattern Recognition Using the RT Method 104

5.2.1 Defi nition of the Unit Space 1055.2.2 Computation of Sensitivity b and Standard

SN Ratio of Unit Space Samples 1075.2.3 Computation of Two Variables Y1 and Y2,

Unit Space Sample by Sample 1085.2.4 Computation of Distances of Unit Space Samples 1095.2.5 Signal Data 1105.2.6 Sample-by-Sample Computation of Sensitivity and

Standard SN Ratio for Signal Data 1115.2.7 Computation of Two Variables, Y1 and Y2,

for Each Signal Sample 1135.2.8 Mahalanobis Distance Computed for Each Sample 1165.2.9 Computation of Mahalanobis Distance for Unknown Data 117

6 EXAMPLES OF ACTUAL APPLICATIONS 123

6.1 Blade Wear Monitoring via Cutting Vibration Waveform (MT Method) 1236.1.1 Purpose of Wear Monitoring 123

x • CONTENTS

6.1.2 Overview of the Cutting Operation and the Vibration Measurement Method 124

6.1.3 Extraction of Features from Waveform Data 1256.1.4 Defi nition of the Unit Space 1266.1.5 Computation of the Mahalanobis Distance (MD)

and Processing of Judgment 1276.2 Appearance Inspection of a Clutch Disk 128

6.2.1 Purpose that the Inspection System Serves 1286.2.2 Composition of Image Capturing Unit 1316.2.3 Conversion of Image Data into Waveform Patterns 1316.2.4 Feature Extraction 1326.2.5 Creation of a Unit Space 1336.2.6 Computation of the Mahalanobis Distance (MD)

and Judgment Processing 1336.2.7 Item Selection and SN Ratio 134

6.3 Monitoring of Machine Conditions (MT Method) 1366.3.1 Purpose of Machine Monitoring and Measured Values 1366.3.2 Defi nition of the Unit Space 1366.3.3 Problems Left Unresolved with the Conventional

Control Format 1376.3.4 Preprocessing and Feature Extraction 1376.3.5 Mahalanobis Distance and Cause Diagnostics 138

6.4 Application to Medical Diagnosis (MT Method) 1406.4.1 Purpose 1406.4.2 Unit Space and Measurement Items 1416.4.3 Comparison of Mahalanobis Distances and Results

of the Diagnoses of Physicians 1426.4.4 Cause Diagnostics of Abnormalities and Their Patterns 143

6.5 Strength Estimation Based on Raw Material Mixing (T Method-1) 1456.5.1 Overview of Raw Material Mixing Problems; Purpose

of Raw Material Mixing 1466.5.2 Defi nition of the Unit Space 1466.5.3 Defi nition of Signal Data 1476.5.4 Normalization of Signal Data 1486.5.5 Computation of Proportional Coeffi cient b and SN Ratio h 1496.5.6 Computation of Integrated Estimated Strength

Value M̂ for Each Data Item 1496.5.7 Computation of Integrated Estimate SN Ratio (db) 1506.5.8 Evaluation of Importance of Items 1516.5.9 Integrated Estimation of Unknown Data 154

CONTENTS • xi

6.5.10 Computation of Integrated Estimate Value Before Normalization 1556.5.11 Summing up the Raw Material Mixing Issues 157

6.6. Real Estate Price Prediction by T Method-1 1606.6.1 Overview of Real Estate Price Prediction and its Purpose 1606.6.2 Defi nition of Unit Space 1616.6.3 Defi nition of Signal Data and Normalization 1616.6.4 Computation of Proportional Coeffi cient b

and SN Ratio h of Signal Data 1626.6.5 Computation of Integrated Estimation of Signal Data 1626.6.6 Analysis of the Impact Green Assets Exert on Rental Fees 1646.6.7 Merits of T Method-1 and Pending Issues 166

APPENDICES

A DIFFERENCES BETWEEN THE MT SYSTEM AND ARTIFICIAL INTELLIGENCE 169

A.1 The Learning Process in the Case of Artifi cial Intelligence 169A.2 Parameters of Artifi cial Intelligence and the Properties

of Recognition Results 170A.3 Differences in Properties Between the MT Method

and Artifi cial Intelligence 170

B DIFFERENCE BETWEEN THE MT SYSTEM AND TRADITIONAL STATISTICAL THEORY 173

B.1 Difference Between the MT Method and the Multivariate Control Chart 173B.1.1 Overview of the Control Chart and the

Multivariate Control Chart 173B.1.2 Differences Between the MT Method and the

Multivariate Control Chart 174B.2 Difference Between Discriminate Analysis

and the MT Method 177B.3 Differences Between T Method-1 and

Multiple Regression Analysis 178B.3.1 Concept Behind Multiple Regression Analysis

and Computation Formula 178B.3.2 Concept Behind T Method-1 180

C SUPPLEMENTARY CONSIDERATIONS CONCERNING MATHEMATICAL FORMULAS 183

D STRATEGY TO USE WHEN DATA INCORPORATES UNMEASURED VALUES 185

xii • CONTENTS

E FUSION WITH ARTIFICIAL INTELLIGENCE AND OTHER RESOURCES 187

E.1 Example of a Cutting Vibration Waveform and Item Selection 187E.2 Application of the Genetic Algorithm 189

F MAHALANOBIS DISTANCE COMPUTATION USING MICROSOFT EXCEL 191

G PALEY’S CONSTRUCT FOR GENERATION OF HADAMARD MATRICE 201

G.1 Quadratic Residue 201G.2 Generation of Paley’s Cyclic Matrix 202

BIBLIOGRAPHY AND REFERENCE SOURCES 207

Bibliography (in English) 207Bibliography (in Japanese) 207References 208

GLOSSARY: DEFINITION OF TERMS 209

INDEX 211

ABOUT THE AUTHORS 219

FOREWORD

We live in uncertain times—uncertain economic times, uncertain political times, and times when everyone is worried about what may happen in the future. Local activities can have regional or even global consequences. But the Mahalanobis-Taguchi (MT) system is a beacon of light enabling us to make some sense of this uncertain information and make some good predictions.

The MT system is a diagnostic and predictive method for analyzing patterns in multivariate data that has provided benefi ts in many diverse applications over the past decade or so. It has proven itself superior in many cases to more traditional artifi cial intelligence applications such as neural nets.

ShoichiTeshima, Yoshiko Hasegawa, and Kazuo Tatebayashi, a group of eminent Japanese mathematicians and engineers have produced Quality Recognition and Prediction: Smarter Pattern Technology with the Mahalanobis-Taguchi System to show how the MT system applies in different ways to benefi t a wide variety of industrial, medical, and other applications.

This book discusses the subject in great detail and yet explains it in such a manner that someone not intimately involved with the fi eld can read the book and understand what pattern recognition is and how the MT system works and how it may be applied.

It starts by giving an explanation of what pattern recognition technology is and how the MT system has been applied to fi elds as diverse as industrial applications, medical applications, and real estate valuation. It then goes on to discuss the merits of the MT system and how data is handled followed by Unit Space and how it may be applied to weather forecasting and rainfall estimation.

After discussing how the Taguchi Method has been used for manufacturing yield prediction the book then goes on to show several detailed applications of the Mahalanobis-Taguchi system from industrial applications such as cutting blade wear and machinery monitoring to real estate price prediction.

Finally the authors close by showing the differences between the MT system and more traditional approaches such as artifi cial intelligence using neural nets and statistical methods.

Yokogawa Electric Corporation sees great potential for using the MT system in process automation applications across a wide range of industries for quality management and improved operations.

Dr. Maurice J. WilkinsVice President, Global Strategic Technology Marketing Center,

Yokogawa Electric Corporation

xiii

PREFACE

The MT System is based on the concept of employing Mahalanobis distance in pattern recognition theory and is the result of the long association that Professor Genichi Taguchi had with the late Indian statistician, Professor P. C. Mahalanobis. Based on ease of use, the MT System is rapidly gaining practitioners in various disciplines in Japan.

The fi rst actual case studies of the MT System were introduced in the 1980s and were primar-ily used in the research of the Quality Engineering Society of Japan. The key areas utilizing the technology at the time were plant and facilities surveillance, parts inspection, medical treatment and pharmaceutics, and the statistical outcomes were extremely impressive. In addition to using Mahalanobis distance for mathematical principles, Dr. Taguchi has proposed a number of pattern recognition applications. While remaining quite simple and straightforward, this has provided enor-mous advantages to the user.

Professor Taguchi has become distinguished around the globe for his Taguchi MethodTM and Robust Design™. In 1997, he was honored in the Automotive Hall of Fame in the U.S.; in 1998, he was designated an honorary member of both the American Society for Quality (ASQ) and the American Society of Mechanical Engineers (ASME).

When asked, “What is it you can do with pattern recognition?” surely most people would respond, “You can predict what something you see or hear is.” We understand traditional pattern recognition to be the process of judging which pattern an object falls into, based on information we already possess.

For example, in the area of medical treatment, a doctor categorizing patients by what disease they might have, among a wide variety of possible conditions, is using a type of pattern recognition that is known by a more familiar term, “diagnosis.” Until now, the fi eld of pattern practice has fol-lowed the same type of understanding. In other words, great importance has been devoted to defi n-ing abnormalities and what abnormalities are attributed to objects.

Professor Taguchi, however, began to believe “pattern recognition is measurement.” From that point of view, the job of the physician becomes one of “measuring how far away the patient is from a healthy state.” For this purpose, the theme of pattern recognition is “how to create a measuring mechanism that calculates the degree of abnormality.”

The fi rst step in creating this measuring mechanism is clear and simple. Normal data for medical treatment is gathered from healthy people. In this situation, abnormal data is not neces-sary. The mechanism to calculate the abnormality level does so using normal data along with mathematical principles. The process of specifying what type of abnormality is offered, then, as a subsequent step.

xv

xvi • PREFACE

The MT System has made pattern recognition a tool that is extremely easy to use. This is largely because those using pattern recognition do not need to focus on the minute details of what kinds of abnormalities exist. Consequently, in terms of recognition precision, the MT System demonstrates results far superior to the other methods.

This work has been compiled with the following target audience in mind. Primary users will include engineers and technicians involved in production and quality control, along with medi-cal practitioners, who will have an interest in the MT System as direct practitioners. Why it is important to accumulate accurate data to achieve the goals, how to measure the data, and what information is necessary have all been explained in the work in detail. Another core user group will be those concerned with pattern recognition research. The book covers all of the mathemati-cal principles of the MT System in depth and, in a straightforward and easy-to-understand way. It compares and contrasts the specifi c features of the system with artifi cial intelligence approaches, etc. The mathematical principles themselves are not that diffi cult to grasp; in fact, it is more impor-tant to understand the actual concepts. The book provides an abundance of examples, conveying practical application and actual procedures in a step-by-step manner. Accordingly, the material will also prove useful to students who wish to understand pattern recognition theory and how it is put into practice in the fi eld.

For many years, the authors have participated in numerous research projects under the tutelage of Dr. Genichi Taguchi. They have listened to the professor’s thoughts and debated various issues with him. The authors have illuminated and expanded upon the system that Professor Taguchi cre-ated. In December of 2008, the authors published An Introduction to the MT System, JUSE Press. This work has earned the support of numerous corporations and universities in Japan. The authors anticipate extensive use of the MT System around the globe in conjunction with the publication of this edition in English.

Shoichi TeshimaYoshiko HasegawaKazuo Tatebayashi

“Taguchi Method(s)®” and “Robust Design™” are trademarks registered in the United States by ASI (American Supplier Institute)

ACKNOWLEDGMENTS

Many people have assisted us in the preparation of the publication of this book. First and foremost, we wish to acknowledge the number one advocate of the MT System, Dr. Genichi Taguchi for his countless contributions to our work as both a technical expert and a philosopher. We have the utmost respect for him and thank him from the bottom of our hearts.

We offer our sincere gratitude to Dr. Tatsuji Kanetaka, physician at the former Tokyo Teishin Hospital and Mr. Sohei Yoshino, real estate appraiser, as well as many others for their contributions to the case study data.

We are grateful to Mr. Shin Taguchi of the American Supplier Institute (ASI) for his support and consultation.

We wish to thank Ms. Hiroko Kobayashi, Ms. Shari J. Berman, and Mr. Terutoyo Taneda of Japan Language Forum for their contributions in translating this work from Japanese to English.

We are grateful to JUSE Press for their support in publishing this book.We thank Mr. Kenji Hasegawa of Yokogawa Electric Corporation, and Mr. Joel Stein and

Ms. Millicent Treloar of Momentum Press for their many kind efforts in coordinating the publication of the English edition.

xvii

1

CHAPTER 1

PATTERN RECOGNITION AND THE MT SYSTEM

What images will the words “pattern recognition” bring to mind for the reader? Many people may think of fi ngerprint verifi cation or voice recognition. These are, in fact, important applications of pattern recognition. Recognition technologies extract a multitude of characteristics from image and voice data and determine if they are consistent with any patterns in the database.

Many instances of pattern recognition deal with classifi cation. However, prediction, as in weather forecasting, is also a matter of pattern recognition. A large amount of meteorological infor-mation that has accumulated right up to the present moment is utilized toward predicting the weather for an unknown block of time that follows, such as the next day. Not only whether it will be clear or rainy, but predictions also include how hot or cold, how wet it will be, etc.

In this way, pattern recognition is a processing procedure that classifi es and predicts based on a large volume of information. This chapter presents a general overview of pattern recognition and various fi elds to which it is applied, followed by a look at the place that the Mahalanobis Taguchi (MT) System occupies in the latest developments of pattern recognition.

1.1 OVERVIEW OF PATTERN RECOGNITION AND THE FIELDS OF APPLICATION

1.1.1 FEATURES OF HUMAN PATTERN RECOGNITION

The illustration in Figure 1.1 shows traced images of the Mona Lisa painting.There are rather subtle differences in drawings (a)–(c). With (a) as the basic pattern, the eyes in

(b) are slightly larger and the mouth is a bit wider in drawing (c). Nonetheless, one would be able to regard each of these as an ordinary drawing of the Mona Lisa.

2 • QUALITY RECOGNITION AND PREDICTION

However, in (d) she has a somewhat sad expression. This is because her mouth is a different shape. When it comes to drawing (e), the relative position of both her eyes and mouth is skewed, making her look like a different person than the Mona Lisa that Da Vinci painted.

Human beings possess the natural ability to be able to look at something and make these dis-tinctions. The ability to look at and distinguish images is called “Pattern Recognition.”

We perform pattern recognition frequently in our day-to-day activities. As part of our everyday behavior, we read written characters; make determinations as to whether we need to take an umbrella with us when going out, or not; make judgments in deciding whether to marry our sweetheart, and so forth. These are all further examples of pattern recognition. The basis of our recognition, or judg-ment, is experience and knowledge, which has accumulated over an entire lifetime.

Since early childhood, we have been patiently taught to read characters, and have become thor-oughly familiar with the character patterns after having written the characters uncountable times. Eventually, we develop an ability to recognize the characters even when they are written by others, when they appear before us in italics, and so on. The acute perception, or “horse sense,” which expe-rienced professionals develop in their specialized fi elds, is one form of pattern recognition.

Reputable doctors can determine their patients’ health conditions or diagnose what they are suffering from simply by looking at their faces. Skilled engineers can identify the trouble with a machine by the sound that it emits.

We are seldom aware of how people perform pattern recognition. It is so taken for granted that most people have never even wondered about the process involved. Nonetheless, it is within the purview of us thinking people to assume that some form of information processing is involved.

Many studies in biochemistry today are shedding a good deal of light on how animals, man included, extract the essential “characteristics” from objects that are required for purposes of rec-ognition, and how the brain performs sophisticated information processing on the basis of such extracted characteristics.

1.1.2 CHARACTERISTICS OF PATTERN RECOGNITION

PERFORMED BY MACHINES

Pattern recognition technology is a technological solution through which man has machinery make judgments and predictions that he would normally perform himself. Accordingly, this pattern

Figure 1.1. Traced images of the Mona Lisa.

(a) (b) (c) (d) (e)

PATTERN RECOGNITION AND THE MT SYSTEM • 3

recognition by machine has been an attempt to mimic acts of recognition by man or animals. Among examples currently in practical use is, in addition to character recognition, individual identity verifi -cation, which utilizes blood veins or iridal patterns. As with man, these technologies require teach-ing data for learning to take place. Next, the group in the database in which the object data (unknown data) can be classifi ed and placed is determined.

There are many ways pattern recognition by machine differs from that of humans. Two are cited here: One is that, in the case of machines, all data is treated as numerical values. The patterns are all converted into numerical data by some means. Images themselves cannot be processed by the computer. The manner in which the given information is converted into numerical data is determined by man. Whether or not an appropriate conversion method is chosen holds the key to the accuracy of pattern recognition.

The second point is that there is a big difference between the two in terms of the manner of learning and recognition processing. In the case of man’s pattern recognition, the information-processing device called the brain, which has been divinely supplied, handles the work. To varying degrees, people’s brains and nerve cells may be structured and function differently from individual to individual, but essentially they all perform the same work. (Of course, when it comes to high-level information processing, the “thinking/reasoning method,” people differ from one another in a rather serious way.)

On the other hand, a variety of recognition methods have been proposed for machines. That means that many types of computation methods have been made available. One representative rec-ognition method is an Artifi cial Neural Network (ANN). And in the context of the ANN, a number of confi gurations and computation methods have been proposed. Another major recognition method is a statistical mathematics approach, which in its own right has yielded a number of computation formulas. Each method has unique characteristics, and various approaches have been utilized on a problem-by-problem basis.

1.1.3 FIELDS THAT USE PATTERN RECOGNITION APPLICATIONS

Pattern recognition is applied quite extensively in diverse areas, as shown below:

Inspection, Diagnostics: Characteristic inspection, visual inspection, medical diagnosisMonitoring: Machinery, equipment, plantsEstimation: Real estate appraisal, corporate worth evaluation, biotechnology evaluationPrediction: Trends in health and illness, trends in economic indices, trends in sales, risk forecastClassifi cation, discrimination: Character recognition, voice recognition, fi ngerprint recognition,

face recognition, unacceptable-mode discrimination

Many countries are faced with problems that include the reduction of the labor force, specialized work forces (engineers), etc. There are high hopes everywhere for the development of pattern recognition that can be utilized extensively in the above-enumerated fi elds, with high practical value.


1.2 STANDARD EXECUTION PROCEDURE FOR PATTERN RECOGNITION

The generally practiced procedure for the execution of pattern recognition, as shown in Figure 1.2, can be broken down broadly into three steps.

Figure 1.2. The standard pattern recognition execution procedure.

(3) Measure

(4) Preprocess

(5) Extract features

(6) Create recognition function

(7) Recognize & discriminate

(8) Diagnose for causes

Step 3Step 2

(1) Clarify purpose

(2) Define normal state

Step 1

1.2.1 DEFINITION OF PURPOSE

Clarify the purpose of the procedure. As it applies to the medical fi eld, the purpose may be to quan-tify the effect of treatment, predict symptoms for the following year, etc. There have been many cases in which those involved became aware halfway through the process that resources were being wasted precisely because the purpose of the procedure had not been clearly defi ned. In the same respect, there are many cases in which, once the purpose is clarifi ed, what to measure becomes clear.

1.2.2 DEFINITION OF THE STANDARD STATE

In the case of character recognition, the readability of the numeral “5” as “5” is the standard. From a pool of handwritten and printed characters, all the characters readable as “5” are picked up and will be defi ned as the standard state for “5.” Defi nition becomes somewhat diffi cult for equipment-monitoring and inspection applications, but, as stated in 1.3.1 (2), focusing attention on the “homogeneity with respect to the purpose” aspect will lead the way for a rational defi nition of the standard-to-be state. Defi ning the standard state is indeed a crucial task that determines the reference point for recognition.

1.2.3 DEFINITION OF THE MEASUREMENT ITEMS

The measurement items include sensor readings as well as data collected from patient interviews. In the interests of achieving the purpose, it is important to acquire suffi ciently accurate data. It happens commonly enough that, despite the prepared list of measurement items, actual measurement does not


proceed with expected accuracy. Since no information processing can follow without observed data, it is necessary, as much as possible, to have appropriate measuring instruments in place. Table 1.1 is a systematically organized list of observed values.

Table 1.1. Various measurement items

Measurement steps Measurement items

Values acquired by sensor measurement

Temperature, relative humidity, electric current, voltage, primary material component values, pressure, torque, tension, revolution; vibration, fl ow amount, electromagnetic fl ux; imagery, luminosity, luminance, spectrum

Daily observed values Share prices, economic indicators, land transaction prices, sales statistics

Other Patient interview data, sensorimetric values, survey data

1.2.4 PREPROCESSING

It is possible to improve the ability to perform discrimination with raw data using noise elimination and render it easier to handle through decomposition and/or division processing or normalization processing.

1.2.4.1 Noise Elimination

One example of noise elimination is leveling. Use of a moving average, a median, etc. will smooth and stabilize a waveform that includes noise. An example of leveling is shown in Figure 1.3. In the processing of image data as well, leveling exhibits great effectiveness.

Figure 1.3. Example of leveling processing (for a moving average).


1.2.4.2 Information Decomposition Processing

It is necessary to extract the information when a fine vibration is not a noise, but part of the feature of a phenomenon. For instance, as in Figure 1.4, with the use of the appropriate method, a given waveform may have to be decomposed into a low-frequency component (using a leveling process) and a high-frequency component to extract the characteristics of each component.

Original waveform

High-freq. componentLow-freq. component (leveling)

Figure 1.4. Decomposition into high-frequency and low-frequency components.

1.2.4.3 Division Processing

Division processing includes division of a given oscillation waveform into appropriate time widths, division of a given image into specifi ed regions, etc. Equipment monitoring produces time-series data of a considerable length, and it is fairly common to divide the data load into so many time widths of data for processing purposes.

1.2.4.4 Normalization Processing

A representative example of normalization is the correction of character pattern size and angle. People correct character size and orientation unconsciously, but specifi c editing and other instruc-tions need to be spoon-fed to the computer.


1.2.5 FEATURE EXTRACTION

People extract the essential features of the object before them unconsciously. For instance, when they see someone’s face, they grasp the contours of the person’s entire face, eyes, and nose. But for pat-tern recognition by computer, the human operator must provide a feature extraction method. Those in the fi eld say, “There is no royal road to characterization,” and this maxim is so true that there is even a unique method to every problem. In the context of feature extraction, technical specialty and identity assume a major signifi cance, and the fi eld has a prominently problem-dependent orientation.

1.2.6 CREATION OF A RECOGNITION FUNCTION

A recognition function is a function for obtaining recognition results on the basis of quantifi ed features. In the context of the MT System, the inverse matrix of the correlation matrix and the esti-mation equation correspond to the recognition function.

1.2.7 RECOGNITION PROCESSING AND JUDGMENT PROCESSING

Recognition processing is, as Figure 1.5 shows, a process of feeding features collected from the object of recognition to the recognition function and obtaining the result as a numerical value. The acquired numerical value is compared against the threshold for judgment to be passed. However, depending on the given problem, threshold-based judgment alone may not be enough, in which case a separate judgment criterion is defi ned on an ad hoc basis. For instance, in the case of monitoring based on time-series data, a result that exceeds the threshold only once will put the judgment on hold; a result that exceeds the threshold continuously will be judged as No Good, that is, “abnormal.”

Figure 1.5. Conceptual illustration of pattern recognition processing.

Unknown (Target) data (x1,x2,…,xk)

Recognition results (numerical value)

Judgment

Recognitionfunction

1.2.8 CAUSE DIAGNOSTICS

Cause diagnostics refers to processing that determines when an object is judged to be abnormal, and to which variable, or combination of variables, the causes are attributable. When this becomes


known, the information gained will make it possible to effectively address the abnormality. Figure 1.6 is a sample display of the result of cause diagnostics. The graph makes it clear that two variables stand out unmistakably as the causes of the abnormality.

Figure 1.6. Sample display of cause diagnostics.

Causes of abnormality

Variables

Con

tribu

tion

to a

bnor

mal

ity

1.3 FIELDS WITH SUBSTANTIAL EXPERIENCE IN THE USE OF MT SYSTEM APPLICATIONS

Many applications of the various computation methods and feature extraction techniques incorporated in the MT System have been made public through presentations at the Quality Engineering Society (Japan) and elsewhere. Some typical examples of such applications are introduced below.

1.3.1 PRODUCT CHARACTERISTICS INSPECTION

1.3.1.1 Application to the Inspection of Electric and Mechanical Characteristics

Electric characteristics are generally measured as a response waveform, and the waveform in many cases contains information usable for distinguishing normal from abnormal. One example given to illustrate this point is the impulse test performed on motors and transformers. An impulse test, as shown in Figure 1.7, is a method used to check for the presence of any abnormality on the basis of the waveform occurring at the time of the release of the voltage applied to the coil.

A normal waveform will produce a locus that is largely homogeneous, but if abnormality is present, the resulting waveform will be distorted. In the past, it was general practice to deter-mine whether the waveform fi t into given bounds, but this method was prone to misjudgment.


Expert operators are familiar with the locus of normal waveforms from long years of experience, and a look at the waveform of a defective product is enough for such workers to conclude that some-thing is amiss with the unit being examined.

With the MT System, the Unit Space is defi ned in terms of a group of waveforms determined to be normal by an experienced operator, and the waveform of the object of inspection is judged as to whether it is good enough to count as a member of the normal group.

In many cases, mechanical characteristics are also measured in a form reduced to response curves or history curves. For example, in the manufacturing or pressure-fi tting of crimp-style ter-minals, pressure history curves can be used to evaluate the presence of any abnormality. A largely regular curve will refl ect a normally foreseen chain of events; if any abnormal event occurs at the time of pressure-crimping, the curve will come out disfi gured.

Figure 1.8 shows an example of a waveform refl ecting the pressure-fi tting of a terminal. Figure (a) indicates a normal history curve, but (b) shows that the characteristic constriction in the curve seen in normal cases is not clearly discernible, so a skilled operator would regard this case as abnormal. Generally, methods based on the use of control limit lines such as those shown in Figure 1.9 have been used, but this approach cannot catch all of the abnormalities.

Using the MT System will make it possible, with a sensitivity that refl ects the experience of skilled operators, to effectively detect all the hidden abnormalities in normal curves.

Figure 1.8. Example of curve for terminal pressure-fi tting.

(a) Normal history curve (b) Curve with abnormalityStroke

No ConstrictionConstriction

Pressure

Figure 1.7. Example of a waveform observed in an impulse test.

Vol

tage

Time


Upper limit

Lower limitPres

sure

Stroke

Figure 1.9. Traditional inspection method.

Other examples include an anticipated upcoming application for abnormality detection in the revolution attenuation (coast-down characteristics) of revolving equipment. In an aircraft engine revolving system inspection application, as shown in Figure 1.10, the inspection for the presence/absence of abnormalities is performed based on post-switched-off engine revolution attenuation curves.

Coast-down time (min.)

Normal bearings

Abnormal

(a) Coast-down characteristics (b) Bearings temperature characteristics with the engine off

Time

Seizing

Rev

olut

ion

Bea

rings

tem

pera

ture

Figure 1.10. Examples of engine characteristic curves.

In the event of the presence of an abnormality, a curve that is at a variance with the normal curve pattern will be drawn. More specifi cally, one type of abnormality would be, as shown in (a), the engine revolution coming to a stop earlier than would be normal, and another type of abnormal-ity, as shown in (b), would be the bearings temperature curve showing a bump at one point. Control limit lines alone do not suffi ce for the purposes of an absolute determination for these issues, and inspection with enhanced accuracy using the MT System is expected to address the issues.

1.3.1.2 Application for Unusual Noise Determination

Inspection of unusual noises to this day is, in many cases, deferred to a human inspector’s auditory function. Figure 1.11 shows a waveform representing a sound emitted by a rotating object. In it, (a) shows the normal sound of rotation, and (b) an abnormal sound.


Figure 1.11. Waveforms of rotation sounds.

(a) Normal (b) Abnormal

There is no other effective means of judgment other than a person’s sense of hearing. Though frequency analysis as Fast Fourier Transform (FFT) and wavelet approaches are used in most cases, they ultimately require human inspectors. These inspectors make judgments based on the visible data. Furthermore, it is diffi cult to catch a noise that occurs only occasionally because the FFT principle is not suitable when dealing with infrequent occurrences. Use of the MT System, however, makes it possible to detect with a high degree of sensitivity any partial disturbance in the waveform.

1.3.1.3 Application to Appearance Inspection

Appearance inspection is one of the most important issues where automation is strongly desired. Figure 1.12 shows the appearance of an automotive clutch disk. Visual inspection for defects such as foreign substances attached to the surface is a widely used procedure at car manufactur-ing factories today; however, as such defects are virtually of the same color as the background material, automation of the procedure has historically been more than something of a challenge. However, with the MT System, the automatic inspection of this procedure is becoming possible.

Figure 1.12. Exterior appearance of a clutch disk.

Crime prevention in and around elevators, homes, etc., danger avoidance for moving automo-biles, and other situations are expected to be increasingly effective with the combined use of image data and the MT System.


1.3.1.4 Application to Spectral Characteristic Inspection

Figure 1.13 shows a spectrographic waveform for a fl uorescent light source. The x-axis corresponds to the wavelength, and the y-axis, to the energy. With the use of the MT System, mass-produced fl uo-rescent lights can be checked with a high degree of precision for spectrographic waveform normalcy.

Figure 1.13. Example of a spectral wavelength.

Wavelength

Ener

gy

The ingredients of petrochemical products and pharmaceutical products, sucrosity and other properties of melons, and such can be expressed in spectrographic waveforms. The MT System has found a new niche of applications in the arena of inspection techniques.

1.3.2 MONITORING OF PRODUCTION PROCESS AND EQUIPMENT

Judgment as to whether a production process is proceeding normally has up to now depended in many cases on control charts (production management schedules), whereby control would be performed based on comparison of given control limit lines. In contrast to this approach, with the MT System, one instance of the “distance from the measured pattern in normal time” is acquired. Characteristic features are extracted from the pattern of measured values that fl ow down the time series. The extracted features are then compared with the normal features. Slight abnormalities, which have not been readable in the past, can now be detected, as well as early signs of any coming abnormality. Furthermore, the MT Sys-tem can be used to predict the characteristics of a new product on the basis of measured values from the production process. Or, the conditions that defi ne a stable production setup for a good-tasting soy sauce can be taken as the normal state to serve as the basis for the manufacturing setting control, so that the factory management can have prior knowledge of the characteristics of an upcoming product.

1.3.3 MEDICAL APPLICATIONS

The fi rst instance of application of the MT System took place in the 1980s, when the system was used in connection with a physical examination. Dr. Tatsuji Kanetaka, then Director of the Gastrointestinal Medicine Department of Tokyo Teishin Hospital, undertook the project, working with Dr. Taguchi.1


The work was designed with the belief in mind that dictated, “If you want to study disease, you should start by collecting data on healthy people.” Dr. Kanetaka used Mahalanobis Distance for this. Traditionally, the normal course of treatment would place emphasis on studying data about the patient, but the team’s starting line lay in the defi ning of a homogeneous group of healthy people.

The upshot of this undertaking by Dr. Kanetaka was the strong suggestion of the possibility of using Mahalanobis Distance to evaluate the therapeutic effect of the treatment administered to his liver disease patient. Since 1994, a number of studies like this in the medical fi eld have been introduced.1 Research has been pursued in studies dealing with the detection of abnormalities in mammographic and medical images, as well. It is said that skillful doctors sharpen their abnormal-ity detection sensibilities by viewing huge numbers of images of cancer-free, normal images. This approach shares common insights with the MT System.

1.3.4 ECONOMIC WORTH ESTIMATION

The price of real estate is determined as a function of a number of factors. The factors are, for example, distance from the nearest train station, area of land, geographic situation of the nearby roads and accesses, surrounding landscape, etc. The price of a given property is estimated in terms of a function of these factors. Historically, regression analysis has been used for these purposes, but high-reliability predictions have been impossible in many cases because of the paucity of property information needed for prediction. Use of the MT System, however, has opened the way for highly accurate property price predictions even under conditions of restricted data availability.

High-reliability appraisal of the economic value of a given corporation, an operation that requires a number of factors to be fed into the computation process, can also be performed with the use of the MT System. Another application that is of interest to many people has to do with share price fl uctuations. However, the variables that determine share prices comprise untold complex ram-ifi cations, which are themselves further complicated by speculative moves, so it is believed that it will be some time before a well-designed, sensible prediction system becomes available in this fi eld.

Also From Momentum Press

• Alarm Management for Process Control: A Best-Practice Guide for Design, Implementation, and Use of Industrial Alarm Systems by Douglas H. Rothenberg

• Advanced Regulatory Control: Applications and Techniques by David W. Spittzer

• Process Control Case Histories: An Insightful and Humorous Perspective from the Control Room by Gregory K. McMillan,

• Protecting Industrial Control Systems from Electronic Threats by Joseph Weiss

• Industrial Resource Utilization and Productivity: Understanding the Linkages, Edited by Anil Mital, Ph.D., and Arun Pennathur, Ph.D.

• Raw and Finished Materials: A Concise Guide to Properties and Applications by Brian Dureu

• Solids Level Measurement and Detection Handbook by Joe Lewis

• And The WBF Series On ISA 88 Implementation Experiences; ISA 95 Implementation Experiences; ISA 88 and ISA 95 in the Life Science Industries; and Applying ISA 88 In Discrete and Continuous Manufacturing

Would You Like To Become an MP Author? Please Contact Joel Stein,[email protected]

For more information, please visit www.momentumpress.net____________________________________________

Th e Momentum Press Digital Library45 Engineering Ebooks in One Digital Library or As Selected Topical Collections

Our ebrary platform, our digital collection features…

• a one-time purchase; not subscription based,• that is owned forever,• allows for simultaneous readers,• has no restrictions on printing, and• can be downloaded as a pdf

The Momentum Press digital library is an affordable way to give many readers simultaneous access

to expert content.

For more information, please visit www.momentumpress.net/library and to set up a trial, please contact [email protected].

ISBN: 978-1-60650-342-3

9 781606 503423

90000

www.momentumpress.net

The Mahalanobis-Taguchi (MT) System is a new theory for pattern recognition and predic-tion. It is based on different concepts and principles from the neural network, or regression analysis, approaches—taking differences in patterns and the degrees of such differences and then adequately quantifying them. Using the MT System in fields such as manufactur-ing, medical treatment, and economics, it is now possible to acquire a broader and more accurate range of information than previous methods were able to supply.

This new book builds on the success of the authors’ previous Japanese-language book, An Introduction to the MT System, to meet the needs of a worldwide readership. For peo-ple who have been heretofore overwhelmed by massive quantities of data, as well as those who wish to study effective recognition and prediction methodology, this break-through book offers:

System into practice.

ABOUT THE AUTHORSDr. SHOICHI TESHIMA -

chi (MT) System to Inspection Technology.” The following year, in 1998, he received an

Dr. Genichi Taguchi. The company produces MT System software and offers consultation

Dr. YOSHIKO HASEGAWA

is the director of Hasegawa P.E. Office, Department Manager of the Quality Control and

Science and Technology, Nihon University. Having studied directly under Dr. Genichi

Taguchi since 1987, Hasegawa is one of the foremost advocates of MT System in Japan

and around the globe. KAZUO TATEBAYASHI is a Quality Engineering consultant and

guest lecturer at The Institute of Statistical Mathematics. He previously served as a man-

-

turer at Meiji University and Tokyo Institute of Technology. Having studied directly under

Dr. Genichi Taguchi since 1976, Tatebayashi is one of the foremost advocates of Quality

Engineering in Japan.

QUALITY RECOGNITION AND PREDICTIONSmarter Pattern Technology with the Mahalanobis-Taguchi System

quality recognition & prediction: smarter pattern technology with the mahalanobis-taguchi system

Documents