FAULT DETECTION AND DIAGNOSIS VIA

IMPROVED MULTIVARIATE STATISTICAL PROCESS CONTROL

NOORLISA BINTI HARUN

UNIVERSITI TEKNOLOGI MALAYSIA

FAULT DETECTION AND DIAGNOSIS VIA

IMPROVED MULTIVARIATE STATISTICAL PROCESS CONTROL

NOORLISA BINTI HARUN

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Master of Engineering (Chemical)

Faculty of Chemical and Natural Resources Engineering

Universiti Teknologi Malaysia

JULY 2005

iii

In the Name of Allah, Most Gracious, Most Merciful.

I humbly dedicate to…

my beloved family members

my best friend

those who has shaped my life and

those who has influenced my life on the right path.

iv

ACKNOWLEDGEMENT

Firstly, I would like to take this opportunity to thank my respectful

supervisor, Assoc. Prof. Dr. Kamarul Asri Ibrahim who has relentlessly given

guidance, constructive ideas and invaluable advice, enabling me to achieve the

objective of this dissertation. In addition I am very grateful to my family members,

no amount of gratitude could repay their kindness of being there as well as the

patience to put up with my whim.

I also would like to thank my fellow course mates and friends for the

assistances, advices and ideas in producing a resourceful, fruitful and practicable

research. May God bless all of you.

v

ABSTRACT

Multivariate Statistical Process Control (MSPC) technique has been widely

used for fault detection and diagnosis (FDD). Currently, contribution plots are used

as basic tools for fault diagnosis in MSPC approaches. This plot does not exactly

diagnose the fault, it just provides greater insight into possible causes and thereby

narrow down the search. Hence, the cause of the faults cannot be found in a

straightforward manner. Therefore, this study is conducted to introduce a new

approach for detecting and diagnosing fault via correlation technique. The correlation

coefficient is determined using multivariate analysis techniques, namely Principal

Component Analysis (PCA) and Partial Correlation Analysis (PCorrA). An industrial

precut multicomponent distillation column is used as a unit operation in this research.

The column model is developed using Matlab 6.1. Individual charting technique such

as Shewhart, Exponential Weight Moving Average (EWMA) and Moving Average

and Moving Range (MAMR) charts are used to facilitate the FDD. Based on the

results obtained from this study, the efficiency of Shewhart chart in detecting faults

for both quality variables (Oleic acid, xc8 and linoleic acid, xc9) are 100%, which is

better than EWMA (75% for xc8 and 77.5% for xc9) and MAMR (63.8% for xc8 and

70% for xc9). The percentage of exact faults diagnoses using PCorrA technique in

developing the control limits for Shewhart chart is 100% while using PCA is 87.5%.

It shows that the implementation of PCorrA technique is better than PCA technique.

Therefore, the usage of PCorrA technique in Shewhart chart for fault detection and

diagnosis gives the best for it has the highest fault detection and diagnosis efficiency.

vi

ABSTRAK

Proses Kawalan Statistik Multipembolehubah (MSPC) digunakan secara

meluas untuk mengesan dan mengenalpasti punca kesilapan. Pada masa kini, carta

penyumbang digunakan untuk mengenalpasti punca kesilapan dalam MSPC. Carta

ini tidak dapat mengenalpasti punca kesilapan dengan tepat yang mana ia sekadar

menunjukkan kemungkinan besar punca kesilapan dan membantu memudahkan

pencarian punca kesilapan. Oleh itu, punca kesilapan tidak dapat ditentukan secara

langsung. Justeru, kajian ini telah dijalankan bagi memperkenalkan satu pendekatan

baru untuk mengesan dan mengenalpasti punca kesilapan melalui teknik korelasi.

Pekali korelasi ditentukan melalui kaedah Analisis Statistik Multipembolehubah iaitu

Analisis Komponen Utama (PCA) dan Analisis Korelasi Separa (PCorrA). Turus

penyulingan multikomponen industri digunakan sebagai operasi unit. Model turus ini

dibangunkan menggunakan perisian Matlab 6.1. Teknik carta individu yang terdiri

daripada Shewhart, Exponential Weight Moving Average (EWMA), dan Moving

Average dan Moving Range (MAMR) digunakan bagi mengesan dan mengenalpasti

punca kesilapan. Berdasarkan kepada keputusan yang diperolehi, kecekapan carta

Shewhart dalam mengesan kesilapan untuk kedua-dua pembolehubah kualiti (Asid

oliek, xc8 dan asid linoleik, xc9) adalah 100% yang mana lebih baik berbanding

dengan carta EWMA (75% bagi xc8 and 77.5% bagi xc9)dan carta MAMR (63.8% bagi

xc8 and 70% bagi xc9). Peratusan mengenalpasti punca kesilapan secara tepat

menggunakan teknik PCorrA bagi carta Shewhart adalah 100% manakala PCA

adalah 87.5%. Ini menunjukkan bahawa penggunaan teknik PCorrA adalah lebih

baik berbanding teknik PCA. Oleh itu, penggunaan teknik PCorrA dalam carta

Shewhart bagi tujuan mengesan dan mengenalpasti punca kesilapan adalah yang

terbaik dengan peratusan kecekapan yang paling tinggi.

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

TITLE PAGE i

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES xii

LIST OF FIGURES xiv

LIST OF SYMBOLS xvi

LIST OF ABBREVIATIONS xx

LIST OF APPENDICES xxi

1 INTRODUCTION

1.1 Introduction 1

1.2 Research Background 2

1.3 Objectives of the Research 4

1.4 Scopes of the Research 4

1.5 Contributions of the research 5

1.6 Chapter Summary 5

viii

2 LITERATURE REVIEW

2.1 Introduction 7

2.2 Definitions of Fault, Fault Detection and Fault Diagnosis 8

2.3 Multivariate Statistical Analysis, MSA 9

2.4 Review on Principal Component Analysis, PCA 10

2.4.1 Extension of Principal Component Analysis, PCA

method

2.4.1.1 Multi-Way PCA 14

2.4.1.2 Multi-Block PCA 15

2.4.1.3 Moving PCA 16

2.4.1.4 Dissimilarity, DISSIM 17

2.4.1.5 Multi-Scale PCA 18

2.4.2 PCA Adopted in Industries 19

2.4.3 PCA as Controller 20

2.4.4 Theory of PCA 21

2.5 Review on Partial Correlation Analysis, PCorrA 23

2.5.1 Theory of PCorrA 26

2.6 Chapter Summary 27

3 IMPROVED STATISTICAL PROCESS CONTROL CHART

3.1 Statistical Process Control, SPC 29

3.2 Traditional Statistical Process Control, SPC chart 30

3.2.1 Shewhart Chart 31

3.2.2 Exponential Weight Moving Average, EWMA Chart 35

3.2.3 Moving Average and Moving Range, MAMR Chart 39

3.3 Improve Statistical Process Control Chart 43

3.4 Chapter Summary 45

ix

4 DYNAMIC SIMULATED DISTILLATION COLUMN

4.1 Introduction 46

4.2 Process Description of Precut Column 48

4.3 The Information of Pre-cut column 49

4.4 Degree of Freedom (DoF) Analysis 51

4.5 Formulations of the Distillation Column Models 56

4.5.1 Mass Balance Equations 56

4.5.1.1 Bottom column model 58

4.5.1.2 Trays model 59

4.5.1.3 Reflux drum model 61

4.5.1.4 Pumparound drum model 62

4.5.2 Equilibrium Equations 63

4.5.2.1 Bubble Point Calculations 66

4.5.3 Summation Equation 68

4.5.4 Heat Balance Equation 68

4.5.5 Hydraulic Equations 69

4.6 Dynamic Simulation of the Study Column 70

4.7 Controller Tuning 74

4.7.1 Bottom Liquid Level Controller 75

4.7.2 Bottom Temperature Controller 76

4.7.3 Top Column Pressure Controller 78

4.7.4 Sidedraw Tray Liquid Level Controller 79

4.7.5 Pumparound Temperature Controller 80

4.7.6 Pumparound Flow Rate Controller 81

4.7.7 Reflux Flow Rate Controller 82

4.7.8 Controller Sampling Time, TAPC 82

4.8 Dynamic simulation Program Performance Evaluation 83

4.9 Chapter Summary 84

x

5 METHODOLOGY

5.1 Introduction 86

5.2 Variable Selection for Process Monitoring 87

5.3 Statistical Process Control Sampling Time, TSPC 90

5.4 Normal Operating Condition (NOC) Data Selection and

Preparation

92

5.4.1 Data pretreatment 94

5.4.2 Correlation Coefficient via Multivariate Analysis

Techniques

94

5.4.2.1 Principal Component Analysis, PCA 95

5.4.2.2 Partial Correlation Analysis, PCorrA 101

5.4.3 Control limits of Improved Statistical Process

Control, ISPC chart

103

5.5 Generated Out of Control, OC data 105

5.6 Fault Coding Monitoring Framework 109

5.7 Performance Evaluation of the FDD Method 111

5.7.1 Fault Detection Efficiency 111

5.7.2 Fault Diagnosis Efficiency 112

5.8 Chapter Summary 113

6 RESULTS AND DISCUSSIONS

6.1 Statistical Process Control Sampling Time, TSPC 116

6.2 Normal Operating Condition, NOC Data 117

6.2.1 Correlation coefficient via Principal Component

Analysis, PCA

119

6.2.2 Correlation coefficient via Partial Correlation

Analysis, PCorrA

123

6.3 Parameters to Design Improved Statistical Process Control

chart

124

6.4 Fault Detection and Diagnosis Method Performance 127

6.4.1 False alarm Analysis 127

xi

6.4.2 Fault Detection Efficiency Results and Results

Discussions

129

6.4.3 Fault Diagnosis Efficiency Results and Results

Discussions

132

6.5 Chapter Summary 136

7 CONCLUSIONS AND RECOMMENDATIONS

7.1 Introduction 138

7.2 Conclusions of Improved SPC framework 139

7.3 Recommendations to Future Research 141

LIST OF REFERENCES 142

APPENDICES 154

xii

LIST OF TABLES

TABLE NO. TITLE PAGE

4.1 Specification of the pre-cut column 49

4.2 Components composition and conditions of the pre-cut column 50

4.3 Number of variables 52

4.4 Number of equations 53

4.5 Control loops properties 55

4.6 Cohen-Coon controller settings 75

4.7 The step change results for bottom liquid level controller 76

4.8 Properties of bottom liquid level P controller 76

4.9 The step change results for bottom temperature controller 77

4.10 Properties of bottom temperature PID controller 77

4.11 The step change results for top column pressure controller 78

4.12 Properties of top column pressure PI controller 78

4.13 The step change results for sidedraw tray liquid level controller 79

4.14 Properties of sidedraw tray liquid level P controller 79

4.15 The step change results for pumparound temperature controller 80

4.16 Properties of pumparound temperature PID controller 81

4.17 Properties of pumparound flow rate P controller 81

4.18 Properties of reflux flow rate P controller 82

5.1 APC variables for each control loops 87

5.2 Normal correlation coefficient for process variables 88

5.3 SPC variables 88

xiii

5.4 The control limits of Improved SPC chart 104

5.5 Parameters to design EWMA 105

5.6 Description of sensor fault, valve fault and controller fault 107

5.7 Improved SPC chart statistic for FDD 108

5.8 Coding system for fault detection 109

5.9 Coding system for fault diagnosis 109

5.10 Fault coding system for Shewhart Chart 110

5.11 Fault coding system for EWMA Chart 110

5.12 Fault coding system for MAMR Chart 110

6.1 Time constant for each control loops 116

6.2 The skewness and kurtosis value of NOC data 118

6.3 Summary of the NOC data 118

6.4 Eigenvectors Matrix 119

6.5 Eigenvalues, variation explained by each PC and cumulative

variation

119

6.6 The correlation coefficient using different cumulative variation 121

6.7 The percentage difference for two PCs retained and three PCs

retained compared to six PCs

122

6.8 Simple correlation matrix 123

6.9 The correlation coefficient result using PCorrA 124

6.10 Constant to design Shewhart range chart 124

6.11 FDD result using different L and β to design EWMA chart 125

6.12 FDD result using PCA with different window size to design

MAMR chart

126

6.13 FDD result using PCorrA with different window size to design

MAMR chart

126

6.14 False alarm result corresponding to oleic acid composition 128

6.15 False alarm result corresponding to linoleic acid composition 128

6.16 The known fault causes 129

6.17 The known out of control, OC conditions 130

xiv

LIST OF FIGURES

FIGURE NO. TITLE PAGE

2.1 Multi-block PCA monitoring framework 15

3.1 Shewhart range and Shewhart individual chart during normal

condition

34

3.2 Shewhart range and Shewhart individual chart during faulty

condition

34

3.3 EWMA control chart under normal condition 38

3.4 EWMA control chart under faulty condition 38

3.5 Moving average and range control chart under normal condition 42

3.6 Moving average and range control chart under faulty condition 42

3.7 The implementation of correlation coefficient, Cjk in SPC chart 44

4.1 Pre cut column 48

4.2 Distillation column control loop 54

4.3 Distillation column schematic diagram 57

4.4 Bubble point calculation procedure 67

5.1 The procedure to calculate and implement TSPC 92

5.2 Example Scree plot 97

5.3 The procedures to determine correlation coefficient, Cjk via PCA

method

100

5.4 The procedures to determine correlation coefficient, Cjk via

PCorrA metho

103

5.5 The outlined of FDD algorithm

115

xv

6.1 The autocorrelation plot 117

6.2 Histogram plot of NOC data 118

6.3 Scree plot 120

6.4 Fault detection efficiency using different Improved SPC chart 131

6.5 Percentage of faults occurs in different region detected by each

Improved SPC chart

132

6.6 Fault diagnosis situation using PCA for oleic acid composition 133

6.7 Fault diagnosis situation using PCorrA for oleic acid

composition

134

6.8 Fault diagnosis situation using PCA for linoleic acid

composition

134

6.9 Fault diagnosis situation using PCorrA for linoleic acid

composition

135

xvi

LIST OF SYMBOLS

aij - Wilson constant

n - Number of retained principal component

A - Square matrix

Ab - Bottom column area

At - tray active area

Ai,Bi,Ci - Antoine constant for component i

A*,B*,C*,D* - Constant for liquid heat capacity

A2, ,'001.D '

999.D - Constant for MA and MR control limit

β - Weighting factor

B - Bottom flow rate

Cj,k - Correlation coefficient between variable j and variable k

δ - Shifted value in standard deviation unit

D - Distillate flowrate

FDetect - Fault Detection

FDiagnose - Fault Diagnosis

vapH∆ - Heat of vaporization

nvapH ,∆ - Heat of vaporization at normal boiling point

hN - Liquid enthalpy leaving each tray

hb - Enthalpy for bottom stream

how - Over weir height

H, h - CuSum control limits

HN - Vapor enthalpy leaving each tray

xvii

I - Identity matrix

cK - Controller Gain

Kp - Static Gain,

L28 - Liquid flowrate at tray 28

LH, P - Pumparound drum height

Lf - Liquid feed flowrate

LH, Re - Reflux drum liquid level

LH, B - Bottom liquid level

L - Width of the control limit

M - Number of observations

MB - Bottom molar hold-up

MW - Molecular weight

MRe - Reflux drum molar hold-up

MP - Pumparound drum molar hold-up

MN - Molar hold up at tray N

N - Number of stages

p - Number of observations

P - Pumparound flowrate

Pci - Critical pressure of component i sat

iP - Vapor pressure for component i

Ptot - Total pressure of the system

ρ - Liquid density

QR - Reboiler heat duty

Q - Number of variables for response variables

R - Correlation matrix

rj,k - Correlation value between variable j and variable k

Re - Reflux flowrate

R - Universal gas constant

rk - Autocorrelation coefficient

xviii

S - Sidedraw flowrate

S - Variance-Covariance matrix

sj,k - Covariance value of variable j and variable k

s - NOC standard deviation

T - Temperature of the system

Tci - Critical temperature of component i

Tri - Reduced temperature of component i

Tb,i - Boiling point for component i

TAPC - APC sampling time

TSPC - SPC sampling time

t, u - Latent vectors

τ - Time Constant

τD - Derivative Time Constant

τI - Integral Time Constant

θ - Dead time

V - Eigenvector matrix

v - Eigenvector or loading vectors

V - Vapor flowrate

Vci - Critical volume for component i

Vi - Liquid molar volume of component i

WL - Weir length

ω - Critical accentric factor of component i

w - Un-normalized eigenvectors

x - NOC mean

1x - Shifted mean

xi - ith observation in the process

xij - Value of the i-th row and j-th column matrix sijx - Standardized variable

x - Liquid phase mole fraction

xix

xS - Sidedraw mole fraction

xRe - Reflux mole fraction

xP - pumparound liquid mole fraction

xD - Distillate mole fraction

γi - Liquid phase activity coefficient for component i

y - Vapor phase mole fraction

zi - The ith EWMA statistic

Zci - Critical compressibility factor for component i

iφ - Vapor fugacity coefficient for component i

Λij - Wilson binary interaction parameter for component i and j

xx

LIST OF ABBREVIATIONS

APC - Automatic Process Control

DoF - Degree of Freedom

EWMA - Exponential Weight Moving Average

FDD - Fault Detection and Diagnosis

ISPC - Improved Statistical Process Control

MA - Moving Average

MESH - Mass, Equilibrium, Summation, Heat

MR - Moving Range

MSA - Multivariate Statistical Analysis

MSPC - Multivariate Statistical Process Control

NIPALS - Non-iterative Partial Least Squares

NOC - Normal Operating Condition

OC - Out of Control

PCA - Principal Component Analysis

PCorrA - Partial Correlation Analysis

PV - Process Variable

QV - Quality Variable

SPC - Statistical Process Control

USPC - Univariate Statistical Process Control

xxi

LIST OF APPENDICES

APPENDIX TITLE PAGE

A Column profile comparison 155

B Parameter selection to design EWMA chart 158

C Parameter selection to design MAMR chart 163

D Number of fault causes 170

CHAPTER 1

INTRODUCTION

1.1 Introduction

A major technological challenge facing the processing industries is the need

to produce consistently high quality product. This is particularly challenging in high

demanding situations where processes are subject to varying raw material properties,

changing market needs and fluctuating operating conditions due to equipment or

process degradation. The need to provide industry with techniques, which enhance

process performance, will require new methodologies to be adopted that are capable

of being used across a spectrum of industrial processing operations and on a wide

range of products (Simoglou et al., 2000).

Modern industrial processes typically have a large number of operating

variables under closed loop control. These loops used to compensate for many types

of disturbances and to counteract the effect of set point change. This is necessary to

achieve high product quality and to meet production standards. Although these

controllers can handle many types of disturbances, there are faults in the process

such as line blockage, line leakage, sensor fault, valve fault and controller fault that

cannot be handled adequately. In order to ensure that process is operating at normal

operating condition as required, faults must be detected, diagnosed and removed.

These activities, and their management, are called as Statistical Process Control, SPC

(Miletic et al., 2004).

2

The combination of Statistical Process Control (SPC) charts and multivariate

analysis approach is used for Fault Detection and Diagnosis (FDD) in the chemical

process. Principal Component Analysis (PCA) and Partial Correlation Analysis

(PCorrA) are the techniques used in this study. A precut multicomponent distillation

column that has been installed with controllers is used as the study unit operation.

Improved Statistical Process Control method is implemented to detect and diagnose

various kinds of faults, which occur in the process.

1.2 Research Background

Statistical methods are used to monitor the performance of the process over

time in order to detect any process shifts from the target. Most of Statistical Process

Control (SPC) techniques involve operations on single response variables such as

weight, pH, temperature, specific gravity, concentration and pressure. This

traditional SPC is used to monitor and verify that the process remains in statistical

control based on small number of variables. The state of statistical control means that

the process or product variables remain close to the target. Normally the fault in the

process is seek through the usage of SPC chart, i.e., the final product quality

variables. Measuring quality variables alone are not enough to describe the process

performance (Kourti et al., 1996). In this study, both quality variables and process

variables are monitored. This can be done by developing the correlation coefficient

between quality variables and process variables using multivariate analysis

technique. In traditional SPC, once the quality variables detected out of statistical

control signal, it is then left up to process operators and engineers to try to diagnose

the cause of out of control using their process knowledge (MacGregor and Kourti,

1995).

Chemical processes are becoming better instrumented with the advances of

process sensors and data acquisition systems. There are massive amount of data

being collected continuously and these variables are often correlated. Multivariate

statistical analysis has been developed in order to extract useful information from

process data and utilize it for process monitoring (Kresta et al., 1991). Normally,

3

multivariate statistical analysis technique is combined with SPC chart using

Hotelling’ s T2 and Q statistics to plot the graph for fault detection and diagnosis.

Even though this conventional method provides good results for fault detection, there

is difficulty in applying this method to diagnose faults because it does not provide a

causal description of the process that can be used as the basis for fault diagnosis.

Currently, contribution plots are used as the basic tools for fault diagnosis in the

multivariate statistical approaches. However, this plot cannot exactly diagnose the

cause but it just provides greater insight into possible causes and thereby narrows the

search. The cause of the faults cannot be found in a straightforward manner.

To overcome the limitation of contribution plot using multivariate analysis

technique to diagnose fault, Improved SPC chart is introduced in this study.

Multivariate analysis approach is applied in the SPC realm procedure to detect and

diagnose the faulty condition in different approach. The multivariate analysis method

is used to develop the control limits of SPC charts. The correlation coefficient

calculated from multivariate analysis technique is applied to improve the SPC chart.

The quality variables data is incorporated in the control chart during the faulty

condition for fault detection purpose, while the process variables which has been

correlated with the quality variables is used for fault diagnosis. Therefore, the

Improve SPC charts applied is not only for quality variables but also for process

variables. By monitoring the process variables, the cause of faulty condition, which

affects the quality variables, could be identified directly. This will help the operator

to take proper action immediately after faults exist in the process.

4

1.3 Objectives of the Research

1. To determine the performance of the Improved Statistical Process Control

charts; which are Shewhart individual and Shewhart range, Exponential

Weight Moving Average (EWMA) and Moving Average and Moving

Range (MAMR) chart for fault detection.

2. To utilize multivariate analysis technique, Principal Component Analysis

(PCA), and Partial Correlation Analysis (PCorrA), for developing the

relationship between the process variable(s) and the quality variable(s) for

fault diagnosis.

1.4 Scopes of the Research

The scopes of the research are:

1. A simulated precut multicomponent distillation column model from

the literature is used as a case study. The model consists of

controllers with various kinds of disturbances and operating

conditions.

2. A set of Normal Operating Condition (NOC) data and a set of Out

of Control (OC) data are generated using this simulated distillation

column.

3. Normal operating statistical model is developed using the

multivariate techniques, PCA and PCorrA via correlation

coefficient approach between the quality variable(s) and the process

variable(s).

4. The Shewhart individual and Shewhart range, Exponential Weight

Moving Average (EWMA), and Moving Average and Moving

Range (MAMR) charts are developed.

5. The faulty condition is incorporated into the process model in order

to see the performance of the control charts to detect the fault(s)

and to find the cause of the fault(s).

5

6. The results of fault detection and diagnosis are then discussed

further.

1.5 Contributions of the Research

In this research, a new approach known as Improved Statistical Process

Control for fault detection and diagnosis is introduced. Multivariate analysis

technique, Principal Component Analysis, PCA and Partial Correlation Analysis,

PCorrA are used to develop the correlation coefficient between quality variables and

process variables. This technique is incorporated in the Improved SPC chart as a

fault diagnosis tools to find the cause of the faulty condition. Improved SPC charts

are applied for both quality variables and process variables which have been

correlated with quality variables of interest.

1.6 Chapter Summary

This thesis contains seven chapters. The first chapter comprises of the

introduction of the research, research background, objectives of the research, scopes

of the research and contributions of this research. Chapter 2 reviews the multivariate

analysis techniques and the concept of multivariate analysis technique used in this

study, which are Principal Component Analysis (PCA) and Partial Correlation

Analysis (PCorrA).

Chapter 3 consists of the concept of Statistical Process Control, SPC chart,

detail explanation on SPC charts; Shewhart, EWMA and MAMR and the way to

construct these charts. This chapter also presents the implementation of multivariate

analysis technique in SPC chart to develop Improve SPC chart.

Chapter 4 expresses the reasons for choosing distillation column as the case

study. This chapter also presents the dynamic modeling of the columns, formulation

6

of dynamic simulating algorithm, establishment of dynamic simulation program,

controller tuning and explanation on Automatic Process Control, APC time

sampling. The performances of the written simulation program are compared with

HYSYS simulator.

Chapter 5 mainly contains the procedure for fault detection and diagnosis.

This chapter contains variables selection to perform process fault detection and

diagnosis, determination of SPC sampling time, generating of normal operating

condition data, out of control data and the development of correlation coefficient

using PCA and PCorrA to relate the quality variables with process variables.

Chapter 6 presents the result and discussion of the research. The results are

systematically presented. Discussions are made on the performance of each

Improved SPC chart to detect known fault in the process and the efficiency of the

proposed method to conduct fault diagnosis with application of multivariate

statistical analysis. Chapter 7 contains the conclusions of the research and

suggestions to the future SPC research activities.

Alw

ower, K. M. (2000). Using exponentially weighted moving average (EWMA)

hen, Q., Kruger, U., Meronk, M. and Leung, A.Y.T. (2004). Synthesis of T2 and Q

statistic for process monitoring. Control Engineering Practice. 12: 745-755.

hen, G., McAvoy, T. J. and Piovoso, M. J. (1997). A multivariate statistical

Chen, K. H. (2001). Data-Rich Multivariate Detection and Diagnosis Using

Eigenspace Analysis. PhD Thesis. Massachusetts Institute Of Technology.

Chen, K. H., Boning, D. S. and Welsch, R. E. (2001). Multivariate statistical process

control and signature analysis using eigenfactor detection method. The 33rd

Symposium on the Interface of Computer Science and Statistics. June 2001. Costa

Mesa, CA. 1996. 1-20.

LIST OF REFERENCES

an, L. C. (2000). Statistical process analysis. New York: McGraw Hill.

Bakshi, B. R. (1998). Multiscale PCA with application to multivariate statistical

process monitoring. American Institute of Chemical Engineering Journal. 44:

1596-1610.

Bakshi, B. R. (1999). Multiscale analysis and modeling using wavelets. Journal of

Chemometrics. 13: 415–434.

B

charts. Minitab Inc., State College, P.A., USA.

C

C

controller for on line quality improvement. Journal of Process Control. 8(2):

139-149.

143

Chiang, L. H., Russell, E. L. and Braatz, R. D. (2000). Fault diagnosis in chemical

processes using Fisher discriminant analysis, discriminant partial least squares,

and principal component analysis. Chemometrics and Intelligent Laboratory

Systems. 50: 243–252

Chiang, L. H., and Braatz, R. D. (2003). Process monitoring using causal map and

multivariate statistics: fault detection and identification. Chemometrics and

liff, N. (1987). Analyzing Multivariate Data. Harcourt Brace Jovanovich

Das

puters and Chemical Engineering.

24: 785-791.

Daszykowski, M., Walczak, B. and Massart, D. L. (2003). Projection methods in

chemistry. Chemometrics and Intelligent Laboratory Systems. 65: 97–112.

DeVor, R. E., Chang, T. H. and Sutherland, J. W. (1992). Statistical quality design

and control: contemporary concepts and methods. New York: Macmillan.

DeVries, A. and Conlin, B. J. (2003). Design and performance of statistical process

control charts applied to estrous detection efficiency. Journal of Dairy Science.

Doty, L. A. (1996). Statistical process control. 2nd Edition. New York: Industrial

Duchesne, C., Kourti, T., and MacGregor, J. F. (2002). Multiv C for startups

and grade transition. American Institute of Chemical Engineering Journal. 48

(12): 2890-2901.

Intelligent Laboratory Systems. 65: 159– 178.

C

Publishers.

h, S. and Venkatasubramanian, V. (2000). Challenges in the industrial

applications of fault diagnostic systems. Com

86: 1970–1984.

press Inc.

ariate SP

144

Finn, J.D. (1974). A general model for multivariate analysis. USA: Holt, Rinehart

and Winston.

Foucart, T. (2000). A decision rule for discarding principal components in

regression. Statistical Planning and Inference. 89: 187-195.

l. 7(6): 403-424.

Gertler, J. J. (1998). Fault detection and diagnosis in engineering systems. New

York: Marcel Dekker.

oulding, P.R., Lennox, B., Sandoz, D.J., Smith, K. and Marjanovic, O. (2000).

ds.

International Journal of Systems Science. 31(11): 1459-1471.

Hashimoto, K., Shimizu, E., Komatsu, N., Nakazato, M., Okamura, N., Watanabe,

basic fibroblast growth factor in schizophrenia.

Psychiatry Research. 120: 211–218.

Himmelblau, D.M. (1978). Fault detection and diagnosis in chemical and

etrochemical processes. Amsterdam: Elsevier Press.

Hu

ion selection. Journal of Process Control. 13: 357–362.

Hunter, J.S (1989). One point plot equivalent to the Shewhart chart with Western

Electris Rules. In: Montgomery, D. C. Introduction to Statistical Quality Control.

3rd Edition. Canada: John Wiley & Sons. 338.

Frank, P. M., and Ding, X. (1997). Survey of robust residual generation and

evaluation methods in observer based fault detection systems. Journal of process

contro

Geankoplis, C. J. (1993). Transport processes and unit operations. 3rd Edition. New

Jersey: Prentice-Hall.

G

Fault detection in continuous processes using multivariate statistical metho

H., Kumakiri, C., Shinoda, N., Okada, S., Takei, N., and Iyo, M. (2003).

Increased levels of serum

p

rowitz, S., Anderson, J., Duvall, M. and Riggs, J. B. (2003). Distillation control

configurat

145

Hwang, D. and Han, C. (1999). Real time monitoring for a process with multiple

operating modes. Control Engineering Practice. 7: 891 – 902.

rahim, K.A. (1997). Application of Partial Correlation Analysis in Active

UTM and IEM. 1997. 434 – 439.

JiJi, R. D., Hammond, M. H., Williams, F. W. and Rose-Pehrsson, S. L. (2003).

Multivariate statistical process control for continuous monitoring of networked

Kahn, M. G., Bailey, T. C., Steib, S. A., Fraser, V. J. and Dunagan, W. C. (1996).

SPC for expert system performance monitoring. Journal of American Medical

. 3(4): 258-269.

Kano, M., Nagao, K., Hasebe, S., Hashimoto, I., Ohno, H., Strauss, R. and

Bakshi, B. R. (2000a). Comparison of statistical process monitoring methods:

f Next Generation

Chemical Plants (PSE Asia 2000). December 6-8. Kyoto, Japan. 345 – 350.

Isermann, R. (1997). Supervision, Fault Detection and Fault Diagnosis Methods – An

Introduction. Control Engineering Practice. 5(5): 639 – 652.

Ib

Statistical Process Control. Proceedings of Regional Symposium of Chemical

Engineering. October 13-15, 1997. Johor:

Jackson, J. E. (1991). A User’s Guide to Principal Components. USA: John Wiley &

Sons.

early warning fire detection (EWFD) systems. Sensors and Actuators B. 93: 107–

116.

Informatics Association

application to the Eastman challenge problem. Computers and Chemical

Engineering. 24: 175-181.

Kano, M., Segawa, T., Ohno, H., Hasebe, S. and Hashimoto, I. (2000b).

Identification of Fault Situations by Using Historical Data Sets. Proceedings of

International Symposium on Design, Operation and Control o

146

Kano, M., Ohno, H., Hasebe, S., Hashimoto, I., Strauss, R. and Bakshi, B. R.

(2000c). Contribution plots for fault identification based on dissimilarity of

process data. American Institute of Chemical Engineering Annual Meeting.

November 12-17. Los Angeles, CA. Unpublished.

Kan ilarity of

Process Data for Statistical Process Monitoring. Proceedings of IFAC Symposium

European Control

Conference (ECC). September 4-7. Porto, Portugal. 1888-1893.

Kano, M., Hasebe, S., Hashimoto, I., Ohno. (2001b). A new multivariate process

monitoring method using principal component analysis. Computers and

Chemical Engineering. 25: 1103-1113.

Kan

. R. (2002). Comparison of multivariate statistical process monitoring

methods with application to the Eastman challenge problem. Computers and

Chemical Engineering. 26: 161-174.

ing, C. J. (1980). Separation processes. 2nd Edition. New York: McGraw Hill.

Kle

analysis and other multivariable methods. Boston: PWS-KENT Publishing

Ko

ations of projection methods for multivariate statistical process control.

Computers chemical engineering. 20: 745-750.

o, M., Nagao, K., Ohno, H., Hasebe, S., Hashimoto, I. (2000d). Dissim

on Advanced Control of Chemical Processes (ADCHEM). June 14-16. Pisa, Italy:

IFAC, 231-236.

Kano, M., Hasebe, S., Hashimoto and Ohno, H. (2001a). Fault Detection and

Identification Based on Dissimilarity of Process Data.

o, M., Nagao, K., Hasebe, S., Hashimoto, I., Ohno, H., Strauss, R. and

Bakshi, B

K

inbaum, D. G., Kupper, L. L. and Muller, K. E. (1987). Applied regression

Company.

urti, T., Lee, J. and Macgregor, J. F. (1996). Experiences with industrial

applic

147

Kourti, T. and McGregor, J.F. (1996). Multivariate SPC methods for process and

product monitoring. Journal of Quality Technology. 1996. 28 (4): 409–428.

e in Engineering. 12: 35-47.

ennox, B., Montague, G. A., Hiden, H. G., Kornfeld, G., and Goulding, P. R.

Loong, L. H., and Ibrahim, K. I. (2002). Improved Multivariable Statistical Process

Control (MSPC) for Chemical Process Fault Detection and Diagnosis (PFDD)–

Cross-Variable Correlation Approach. Regional Symposium on Chemical

Engineering, RSCE/SOMChem. 1637-1644.

Lopes, J. A. and Menezes, J. C. (1998). Faster Development of Fermentation

Processes. Early Stages Process Diagnosis. American Institute of Chemical

Engineering Journal. 94( 320): 391-396.

Luyben, W. L. (1963). Process Modeling, Simulation, and Control for Chemical

Engineer. USA: McGraw Hill.

MacGregor, J. F., Jaeckle, C., Kiparissides, C. and Koutoudi, M. (1994). Process

Monitoring and Diagnosis by Multi-block Methods. American Institute of

Chemical Engineering Journal. 40: 826–838.

Kresta, J., MacGregor, J.F and Marlin, T.E. (1991). Multivariate statistical

monitoring of process operating performance. Canada journal chemical

engineering. 69: 35-47.

Leger, R. P., Garland, Wm. J.and Poehlman, W. F. S. (1998). Fault detection and

diagnosis using statistical control charts and artificial neural networks. Artificial

Intelligenc

Lennox, B., Goulding, P.R. and Sandoz, D.J. (1999). Analysis of multivariate

statistical methods for continuous systems. Computers and Chemical

Engineering. 23: 207-210.

L

(2001). Application of multivariate statistical process control to batch operations.

Biotechnology and Bioengineering. 74(2): 125-135.

148

MacGregor, J.F. and Kourti, T. (1995). Statistical process control of multivariate

processes. Control engineering practice. 3(3): 403-414.

Marriott, F. H. C. (1974). The Interpretation of Multiple Observations. N.Y.:

Academic Press.

Martin, E.B., and Morris, A.J. (1996).

process performance monitoring charts. Journal process control. 6(6): 349-358.

Martin, E. B., Morris, A.J., Papazoglou, M. C., and Kiparissides, C. (1996). Batch

Computers Chemical Engineering.

20: 599-604.

Martin, E Morris, Kipar 1999 ng performance

enhancement through multivariate process c Annual Reviews in

Contro 3: 35-44

Mason, R . and Yo (2002 iate stati ocess control with

industr applica : Asa

Miles, J. and Shevlin, M. (2001). Applying regression and correlation: A guide for

studen nd resea ndon: cation.

Miletic, uinn, S , M., Vaculik, V., and

Review:An indus ective ementing on-line applications of

multiv te statis l of P trol. 14: 821-836.

Misra, M ue, H. S. J., , C. (20 ultivariate process

monitoring and fa sis by le PCA. ters and Chemical

Engineering. 26: 1281–1293.

Montgom D. C. (1 duction to Statistical Quality Control. 3rd Edition.

Canada: John Wiley & Sons.

Non-parametric confidence bounds for

process monitoring for consistent production.

. , B. A.J., and issides, C. ( ). M turianufac

statistical ontrol.

l. 2 .

. L ung, J. C. ). Multivar stical pr

ial tions. U.S.A -Siam.

ts a rchers. Lo Sage Publi

I., Q ., Dudzic Champagne, M. (2004).

trial persp on impl

aria tics. Journa rocess Con

., Y H., Qin, and Ling 02). M

ult diagno multi-sca Compu

ery, 996). Intro

149

Nash, J.C. and Lefkovitch, L.P. (1976). Principal component and regression by SVD

on a small computer. Apply Statistics. 25(3): 210-216.

Nomikos, P. and MacGregor, J. F. (1994). Monitoring of batch processes using

Multi-W ponent Analysis. American In f Chemical

Engine ing Journa : 1361 –

Nomikos, (1996). D nd diag abnormal batch operations based on

multi-way principa ent anal Transact :25-266.

Nong Y., Qiang C., Syed Masum E., a tae N. (20 i-square Statistical

Profil for Ano tion. Proceedings of the 200

Inform n Assur ecurity States Mi cademy. June 6-7,

2000.

Oakland, . (1996) l Process Control. 3rd Edition. Oxford: Butterworth

Heine n.

Ogunnaik .A. and (1994) s Dynamic elling and Control.

Oxford University

Onat, A., soy, V. l, O. (1 aist circu

ratio in Turkish adults: interrelation her risk fa d association with

cardio ular dise national Journal of Cardio 0: 43–50.

Patton, R., Frank, P. and Clark, R. (1989). Fault diagnosis in dynamic systems:

Theory and application. UK: Prentice Hall.

Quemerais, B., Cossa, D., Rondeau, B., Pham, T. T. and Fortin, B. (1998). Mercury

distribution in relation to iron and manganese in the waters of the St. Lawrence

river. The Science of the Total Environment. 213: 193-201.

Quesenberry, C. P. (1997). Statistical process control methods for quality

improvement. New York: John Wiley.

ay Principal Com stitute o

er l. 40 (8) 1375.

P. etection a nosis of

l compon ysis. ISA ions. 35

nd Kyu 00). Ch

ing maly Detec 0 IEEE Workshop on

atio ance and S United litary A

West Point, NY: IEEE. 2000. 187-193.

J. S . Statistica

man

e, B Ray, W.H. . Proces s, Mod

Press, Inc.

San and Uysa 999). W mference and waist-to-hip

with ot ctors an

vasc ase. Inter logy. 7

150

Ralston, P., DePuy, G., and Graham, H. (2001). Computer-based monitoring and

fault diagnosis: a chemical process case study. ISA transaction. 40: 85-98.

Reid, R.C., Prausnitz, J. M. and Poling, B. E. (1987). The properties of gases and

liq ew Yo raw Hill

Ruiz, D., Nougues, J. M., and Puigjaner, L. (2001). Fault diagnosis support system

for complex chemical plan ters and l Enginee : 151-

Santen, A., Koot, G. L. M., and Zullo, L. C. (1997). Statistical data analysis of a

chemical plant. Computers Chemical Engineering. 21: 112-1129.

Seborg, D. E., Thomas, C. and Tetsuya, W. (1996). Principal component analysis

ndustrial distillation column. 13th World

IFAC Congress. 1996. San Francisco.

Sha pplied Mul chniques. USA: John Wiley & Sons.

Shirley, M. D. F., Rushton, S. P., Sm G. C., South, A. B., and Lurz, P. W. W.

(2003). Investigating the spatial dynamics of bo rculosis in

evaluating an ual-based ion model.

7: 139–157.

Shinskey, F. G. (1984). Distillation control for productivity and energy conservation.

2nd Edition. New York: MacGraw Hill.

Simoglou, A., Martin, E. B., and Morris, A. J. (2000). Multivariate statistical process

control of an industrial fluidised-bed reactor. Control Engineering Practice. 8:

893-909.

Singh. R., and Gilbreath, G. (2002). A real time information system for multivariate

statistical process control. International Jurnal of Production Economics. 75:

161-172.

uids. 4th Edition. N rk: MacG .

ts. Compu Chemica ring. 25

160.

applied to process monitoring of an i

rma, S. (1996). A tivariate Te

ith,

vine tube badger

populations: individ simulat Ecological

Modelling. 16

151

Smith, J. M., Van Ness, H. C., and Abbott, M. M. (1996). Introduction to chemical

engineering thermodynamics. 5th Edition. New York: MacGraw Hill.

Souza, A. M., Samohyl, R. W., and Malave, C. O. (2004). Multivariate feed back

control: an application in a productive process. Computers & Industrial

Engineering. 46: 837–850.

Stephanopoulos, G. (1984). Chemical Process Control: An Introduction to Theory

and Practice. New Jersey: Prentice Hall.

Srivastava, M.S. (2002). . New York: John Wiley

and Sons.

Summers, D. C. (2000). Quality. 2nd Edition. Upper Saddle River, N.J.: Prentice

Hall.

Thorndike, R. M. (1976). Correlational procedures for research. New York:

Gardner Press.

Tsumoto, S., Hirano, S., Yasuda, A. and Tsumoto, K. (2002). Analysis of amino-

acid sequences by statistical technique. Information Sciences. 145: 205–214.

Tubb, C., Omerdic, E. (2001). Fault Detection and Handling. Improves technical

report FD 001. 1-8.

Upadhyaya, B.R., Zhao, K., and Lu, B. (2003). Fault monitoring of nuclear power

plant sensors and field devices. Progress in Nuclear Energy. 43: 337-342.

Venkatasubramanian, V., Rengaswamy, R., Yin, K., and Kavuri, S. N. (2003a). A

review of process fault detection and diagnosis Part I: Quantitative model-based

methods. Computers and Chemical Engineering. 27: 293-311.

Methods of Multivariate Statistics

152

Venkatasubramanian, V., Rengaswamy, and Kavuri, S. N. (2003b). A review of

process fault detection and diagnosis Part II: Qualitative models and search

strategies. Computers and Chemical Engineering. 27: 313-326.

Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., and Yin, K. (2003c). A

review of process fault detection and diagnosis Part III: Process history based

methods. Computers and Chemical Engineering. 27, 327-346.

Walas, S. M. (1985). Phase equilibrium in chemical engineering. Butterworth

Publisher.

Wang, J. C. and Henke, G. E. (1966). Tridiagonal Matrix for Distillation.

Hydrocarbon Processing. 45(8): 155 – 165.

Wang, S. and Xiao, F. (2004). Detection and diagnosis of AHU sensor faults using

principal component analysis method. Energy Conversion and Management. 45:

2667-2686.

Wang, H., Song, Z. and Wang, H. (2002). Statistical process monitoring using

improved PCA with optimized sensor locations. Journal of Process Control. 12:

735-744.

Wetherill, W.B. and Brown, D. (1991). Statistical process control for the process

industries. 3rd Edition. London : Chapman and Hall.

Wise, B. M., and Gallagher, N. B. (1996). The process chemometrics approach to

process monitoring and fault detection. Journal of Process Control. 6: 329-348.

Wissemeier, A. H. and Zuhlke, G. (2002). Relation between climatic variables,

growth and the incidence of tipburn in filed-grown lettuce as evaluated by

simple, partial and multiple regression analysis. Scientia Horticulturae. 93: 193-

204.

153

Woodall, A. H. (2000). Controversies and contradictions in Statistical Process

Control. Journal of Quality Control. 30(4): 341-350.

Yoon, S. and MacGregor, J.F. (2000). Statistical and causal model-based approaches

to fault detection and isolation. American Institute of Chemical Engineering

Journal. 46(9): 1813-1824.

Yoon, S. and MacGregor, J.F. (2001). Fault diagnosis with multivariate statistical

model part I: Using steady state fault signatures. Journal of process control. 11:

387-400.

Yoon, S., Kettaneh, N., Wold, S., Landry, J. and Pepe, W. (2003). Multivariate

process monitoring and early fault detection (MSPC) using PCA and PLS. Plant

automation and decision support conference. September 21-24, 2003.

Yung, W. K. C. (1996). An integrated model for manufacturing process

improvement. Journal of Materials Processing Technology. 61: 39-43.

Washington, DC: National Petrochemical and Refiners Association. 2003. 1-18.