recent research activities in laboratory
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Recent Research Activities in Laboratory. Intelligent Mechanics Laboratory. School of Mechanical Engineering College of Engineering Pukyong National University San 100 Yong dang-dong Nam-gu, Pusan 608-739, Korea - PowerPoint PPT PresentationTRANSCRIPT
Recent Research Activities in Laboratory
Recent Research Activities in Laboratory
Intelligent Mechanics LaboratoryIntelligent Mechanics Laboratory
School of Mechanical Engineering College of Engineering Pukyong National University
San 100 Yong dang-dong Nam-gu, Pusan 608-739, Korea
Tel) 82-51-620-1604, 625-1604
Fax)+ 82-51-620-1405
E-mail) [email protected]
Home page: http://vibration.pknu.ac.kr
School of Mechanical Engineering College of Engineering Pukyong National University
San 100 Yong dang-dong Nam-gu, Pusan 608-739, Korea
Tel) 82-51-620-1604, 625-1604
Fax)+ 82-51-620-1405
E-mail) [email protected]
Home page: http://vibration.pknu.ac.kr
Faculty :
Prof. Bo Suk Yang, Ph.D.
Prof. Soo-Jong Lee, Ph.D.
Prof. Dong-Jo Kim, Ph.D.
Faculty :
Prof. Bo Suk Yang, Ph.D.
Prof. Soo-Jong Lee, Ph.D.
Prof. Dong-Jo Kim, Ph.D.
Students :
Ph.D. : 8
Sung-Pil Choi, Jang-Woo Lee, Dong-Soo Lim, Young-Chan Kim, Yong-Han Kim, Jing-Long An, Jun-Ho Park, Woo-Kyo Jang
M.S. : 6
Yong-Min-Oh, Soo-Mok Lee, Jin-Dae Song, Bum-Jung Han, Young-Ho Choi, Tian Han
Undergraduate: 6
Students :
Ph.D. : 8
Sung-Pil Choi, Jang-Woo Lee, Dong-Soo Lim, Young-Chan Kim, Yong-Han Kim, Jing-Long An, Jun-Ho Park, Woo-Kyo Jang
M.S. : 6
Yong-Min-Oh, Soo-Mok Lee, Jin-Dae Song, Bum-Jung Han, Young-Ho Choi, Tian Han
Undergraduate: 6
Alumnus(1986 ~2000) Alumnus(1986 ~2000)
Ph.D. M.S. B. Eng.
7 50 82
Ph.D. M.S. B. Eng.
7 50 82
Faculty & Student
Research Area
Rotating Machinery(Pump,Motor,Turbine Generator, Compressor, etc.)
Rotating Machinery(Pump,Motor,Turbine Generator, Compressor, etc.)
Dynamic Optimum DesignDynamic Optimum Design
Monitoring & DiagnosticsMonitoring & DiagnosticsVibration AnalysisVibration Analysis
Area of Research:
Rotordynamics & Vibration Analysis
Intelligent Optimum Design & System Identification
Intelligent Condition Monitoring & Diagnostics
Area of Research:
Rotordynamics & Vibration Analysis
Intelligent Optimum Design & System Identification
Intelligent Condition Monitoring & Diagnostics
List of Research Applications:
Integrated Classification Techniques for Vibration Diagnostics
Development of Case-Based Reasoning Algorithm
Vibration Diagnostics by Petri-Net Algorithm
Model Updating Using Artificial Neural Networks
Development of Enhanced Genetic Algorithm for Optimum Design
Development of Optimization Algorithm Using Artificial Life
List of Research Applications:
Integrated Classification Techniques for Vibration Diagnostics
Development of Case-Based Reasoning Algorithm
Vibration Diagnostics by Petri-Net Algorithm
Model Updating Using Artificial Neural Networks
Development of Enhanced Genetic Algorithm for Optimum Design
Development of Optimization Algorithm Using Artificial Life
Research Area
Methods & Tools:
Artificial Neural Network (SOFM, LVQ, RBF, etc)
Random Tabu Search Method
Genetic Algorithm, Immune-Genetic Algorithm
Artificial Life
Methods & Tools:
Artificial Neural Network (SOFM, LVQ, RBF, etc)
Random Tabu Search Method
Genetic Algorithm, Immune-Genetic Algorithm
Artificial Life
Applications :
Optimum Shape Design of Rotor Shaft
Optimum Design for Bearing & Seal Geometry
Optimum Layout of Damping Material
Optimum Allocation of Piping System
Sensitivity Analysis
Applications :
Optimum Shape Design of Rotor Shaft
Optimum Design for Bearing & Seal Geometry
Optimum Layout of Damping Material
Optimum Allocation of Piping System
Sensitivity Analysis
Dynamic Optimum DesignDynamic Optimum Design
Vibration Analysis
Software Development for Vibration Analysis
Horizontal Pumps and Vertical Pumps
(General Centrifugal Pump, Boiler Feedwater Pump)
Hydraulic Turbine-Generator Rotor System for Hydro-Power Plant
Steam Turbine/Generator System for Thermal & Nuclear Power Plant
Rotary Compressor Rotor System for Small Refrigerator
Software Development for Vibration Analysis
Horizontal Pumps and Vertical Pumps
(General Centrifugal Pump, Boiler Feedwater Pump)
Hydraulic Turbine-Generator Rotor System for Hydro-Power Plant
Steam Turbine/Generator System for Thermal & Nuclear Power Plant
Rotary Compressor Rotor System for Small Refrigerator
Vibration Analysis
Motor/Generator Rotor System with Electromagnetic Pull
Geared & Coupled System (Bending & Torsional Vibration)
Vibration Analysis
Motor/Generator Rotor System with Electromagnetic Pull
Geared & Coupled System (Bending & Torsional Vibration)
Development of Vibration Diagnostics Algorithms
Neural Network & Fuzzy Theory
Decision Tree & Decision Table
Expert System
Petri Net Technique
Development of Condition Monitoring System
Case-Based Reasoning & Diagnosis
Construction of Case Base by Vibration Troubleshooting
Web Site of Case Base Search(http://vibration.pknu.ac.kr)
Wavelet Analysis & Feature Extraction
Ball Bearing Defect, Rubbing
Development of Vibration Diagnostics Algorithms
Neural Network & Fuzzy Theory
Decision Tree & Decision Table
Expert System
Petri Net Technique
Development of Condition Monitoring System
Case-Based Reasoning & Diagnosis
Construction of Case Base by Vibration Troubleshooting
Web Site of Case Base Search(http://vibration.pknu.ac.kr)
Wavelet Analysis & Feature Extraction
Ball Bearing Defect, Rubbing
Condition Monitoring & DiagnosticsCondition Monitoring & Diagnostics
Vibration Analysis of Turbine-Generator System for Nuclear Power Plant
HP Rotor LP Rotor
Turbine/Generator Rotor System
Steam Turbine-Generator Shaft Model (Kori #3 & 4, 1007MW)
Vibration Analysis : Campbell Diagram
Vibration Analysis : Damping Ratio & Root Locus
Vibration Analysis : Mode Shape
Damping and Stiffness Coefficients of No. 2 Bearing
Vibration Analysis : Bearing Dynamic Coefficients
Vibration Analysis : Comparison of Natural Frequency & Error
0 500 1000 1500 20001E-5
1E-4
1E-3
0.01
0.1
1
#5 bearing
#9 bearing
Am
plitu
de (m
m)
Rotating speed (rpm)
Comparison of unbalance response at bearing No.5, 9
Vibration Analysis : Unbalance Response
Vibration Analysis of Turbine-Generator System for Pumped-Storage Power Plant
Pump\Turbine-Generator\Motor Shaft Model (Muju #1, 2, 336MW)
0 100 200 300 400 500 600 700 8000
500
1000
1500
2000
2500 Runaway speed
Operating speed
1X
9XN
atu
ral
freq
uen
cy (
cpm
)
Rotating speed (rpm)
Vibration Analysis : Campbell Diagram
100 200 300 400 500 600 700 800
1
10
Operating speed
Generator/motor
Runner
without add mass/magnetic force
with add mass/magnetic force
Am
pli
tud
e (
m)
Rotating speed (rpm)
100 200 300 400 500 600 700 800
0.01
0.1
1
10
Operating speed
Generator/motor
Runner
Am
pli
tud
e (
m)
Rotating speed (rpm)
100 200 300 400 500 600 700 800
0
500
1000
1500
2000
2500
3000
Operating speed
without add mass of water
with add mass of water
Nat
ura
l fr
equ
ency
(cp
m)
Rotating speed (rpm)
Hydraulic unbalance
Mechanical unbalanceMode shape
Add-mass effect of water
Vibration Analysis : Mode Shape & Unbalance Response
Analytical Model & Earthquake Wave
0 5 10 15 20 25-600
-400
-200
0
200
400
600
Acc
eler
atio
n (g
al)
Time (sec)0 2 4 6 8 10
0
2
4
6
8
10
12
Frequency (Hz)
Am
plit
ude
Turbine-generator model and KOBE earthquake wave(EW)
Seismic Response Analysis
-0.4
-0.2
0.0
0.2
0.4
No.2 bearing
-0.4
-0.2
0.0
0.2
0.4
No.5 bearing
Dis
pla
cem
ent
(mm
)
0 5 10 15 20
-0.4
-0.2
0.0
0.2
0.4
No.9 bearing
Time (sec)
0 5 10 15 20
-0.6
-0.4
-0.2
0.0
0.2
0.4
No.9 bearing
Time (sec)
-0.4
-0.2
0.0
0.2
0.4
Dis
pla
cem
ent
(mm
)
-0.4
-0.2
0.0
0.2
0.4
0.6
No.5 bearing
No.2 bearing
Modal superposition method
Direct integration method
Seismic Response : Comparison of Analysis Methods
0 50 100 150 200 250
2
4
6
8
10
12
14
16
2
Max. allowable stress14.614
GENLPCLPBLPAHPB
end
ing
str
ess
(MN
/m )
Station number
Distribution of bending stress at maximum displacement position
Seismic Response Analysis : Bending Stress
0 10 20 30 400.0000
0.0002
0.0004
0.0006
0.0008
10
Level
6
7
8
9
Am
plit
ude
Time(sec)
Wavelet transform of seismic response wave at bearing No.9
0 10 20 30 400.000
0.001
0.002
10
Level
6
7
8
9
Am
plitu
de
Time(sec)
EW component UD component
Seismic Response Analysis: Wavelet Transform
Flow Chart for Immune Genetic Algorithm (IGA) Flow Chart for Immune Genetic Algorithm (IGA)
StartStart
Production of the initial chromosome calculation of the fitness
Production of the initial chromosome calculation of the fitness
Calculation of the fitness and affinity of individuals
Calculation of the fitness and affinity of individuals
Production of the individuals
Production of the individuals
SelectionSelection
Crossover and Mutation
Crossover and Mutation
Change old population with new population
Change old population with new population
Gen Max.GenGen Max.Gen
EndEnd
No
Yes
The differentiation of memory and suppressor cell
The differentiation of memory and suppressor cell
Calculation of the affinity between individuals and suppres
sor cellsAffinity Tacl
Calculation of the affinity between individuals and suppres
sor cellsAffinity Tacl
Proliferation andSuppression of
individuals
Proliferation andSuppression of
individuals
Optimization Result of IGAOptimization Result of IGA
IGAInitial
value Weight, W Bearing force, bF3
Critical speed, cn (rad/s)
c2 685.84 538.34573 549.86c3 2646.14 2309.7938 2301.05
Shaft weight, W (kg)
W 10.235 5.59691 9.82036
Transmitted force, bjF (N)
bF1 13.562 9.808 15bF2 475.599 339.90555 543.1bF3 461.366 183.49775 114.71
Maximum bending stress, (MPa)
MaxBS 45.29 22.056 35.75
Maximum amplitude, (m)
MaxAmp 78.9 65.87 68.9(b) Original model
(a) Optimum model
Flow Chart for Enhanced Genetic Algorithm Flow Chart for Enhanced Genetic Algorithm
StartStart
Production of the initial chromosome calculation of the fitness
Production of the initial chromosome calculation of the fitness
Calculation of the fitness Calculation of the fitness
Production of the individualsProduction of the individuals
SelectionSelection
Crossover and MutationCrossover and Mutation
Calculation of the affinity between saved candidacy solution set
Calculation of the affinity between saved candidacy solution set
FAC = 1 FAC = 1
Yes
No
Affinity < 0.1Affinity < 0.1
Decision and reallocation of candidacy solution
Decision and reallocation of candidacy solution
Erasion candidacy solution
Erasion candidacy solution
No
Selection and CrossoverSelection and Crossover
Production of the individualsProduction of the individuals
Fmin = Fmax Fmin = Fmax
Yes
Change old population with new population
Change old population with new population
No
Yes
EndEndGlobal search Local search
Change old population with new population
Change old population with new population
Solution num. = NSolution num. = NNo
Yes
Yes
Affinity < 0.1
Affinity < 0.1
Selection next solutionSelection next solution
No
),( yxf
x y
)5.3cos3cos1.2()1.25.2cos2(cos),( yyxxyxf
Optimum values
SGA 16.091155
IGA
16.09171316.09171316.09105116.091051
EGA
16.09172016.09172016.09172016.091720
Comparison of Optimization ResultsComparison of Optimization Results
Characteristics of Artificial life Algorithm
Circular food chain Dynamic interaction in the environment
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
xs
Artificial worldx(x
1
max, x2
max )
x(x1
min, x2
min )
x2
x1
D
Neighborhood region(acting radius)
: Resource: Artificial organism
Step 1 Initialization
Step 2 Search resource
Step 3 Movement using elite reservation strategy
Step 4 Metabolism
Step 5 Increasing age
Step 6 Reproduction
Step 7 Reducing energy
Step 8 Increasing generation
Flow chart for Artificial Life Algorithm
)0.2,0.2()0.1()(0.100),( 2122
122
1221 xxxxxxxf
Emergent Colonization produced at the optimumpoint
= (1.0, 1.0) optx )( optf x = 0
Contour line and emergent colonization for banana function
Optimization result of Artificial life Algorithm
Emergent Colonization produced at the optimumpoint
Contour line and emergent colonization for camel function
)0.2,0.2(
)0.40.4()3/1.20.4(),(
21
22
22
21
41
2121
xx
xxxxxxxf
= {(0.0898, -0.7126), (-0.0898, 0.7126)} optx )( optf x = -1.0316
Optimization result of Artificial life Algorithm
Structure of Classification System for Diagnostics
Database Storage Module Database Storage Module
Data Training Module Data Training Module
Classification Module Classification Module
1. Experimental Configuration
2. A/D Converting System
3. Database Management
1. Experimental Configuration
2. A/D Converting System
3. Database Management
1. Wavelet Transform
2. Statistical Feature Extraction
3. Training using Neural Network
1. Wavelet Transform
2. Statistical Feature Extraction
3. Training using Neural Network
1. Untrained New Data
2. Store to Database
1. Untrained New Data
2. Store to Database
A/D ConversionA/D Conversion
Hardware
Transient Stable
Software
Signal Data, Condition, Specification,Date, Sensor Information
Database
W/T TransformFeature Extraction
W/T TransformFeature Extraction
Training (Neural Network) :SOFM, LVQ
Training (Neural Network) :SOFM, LVQ
W/T TransformFeature Extraction
W/T TransformFeature Extraction
Trained DataCondition ClassificationCondition Classification
Software Process
Software
Integrated Classification System for Diagnostics
Normal Abnormal
Wavelet Transform
Statistical Evaluation Value : Mean, Standard deviation, Skewness, Kurtosis
Statistical Evaluation Value : Mean, Standard deviation, Skewness, Kurtosis
More & Robust Features than Time-waveform
Neural Network Classification
Training Data
Trained Data
Self-Organizing Feature Map & Learning Vector Quantization
Technique
Re-organized into CODEBOOK Vectors
k-NN TechniqueWhich Class?
Untrained Data
Main Window for Diagnosis System
Database Storage Module : Data Input
Database Storage Module : Management
Diagnosis Module : Classification
Introduction of Case-Based Reasoning System
- Memorize previous situation and case-history - Reuse to for solving new problem - Previous problem solving current problem solving
- Memorize previous situation and case-history - Reuse to for solving new problem - Previous problem solving current problem solving
CBR System for Vibration Diagnosis
• Categories and Details : Can be Added through CBR Cycle
• Keywords and Weights : Extracted from the Case-Base and Stored to Library
Input and Output of CBR System
http://vibration.pknu.ac.kr
Petri Net Algorithm for Abnormal Diagnosis
BackgroundBackground
Cause Diagnosisby Symptom
Frequency
Cause Diagnosisby Symptom
Frequency
Occurrence of Abnormal Vibrationat Rotating Machine
Occurrence of Abnormal Vibrationat Rotating Machine
Expression ofSymptom Freq.by Target Transition
Expression ofSymptom Freq.by Target Transition
Addition of Source Transition
at All SourcePlaces
Addition of Source Transition
at All SourcePlaces
Minimal Support
T-invariantCalculation
Minimal Support
T-invariantCalculation
Petri NetCarl A. Petri
1962
Petri NetCarl A. Petri
1962
PUKYONG NATIONAL UNIV. Intelligent Mechanics Lab.
Diagnosis of Rotating Machine by Petri Net
ModelingModelingModelingModeling
Place Transition
P1 축상에서 헛도는 베어링의 기계적 느슨함
P2 하우징내에서 베어링 느슨함
P3 과도틈새
P4 회전속도의 많은 회전성분
P5 구별되는 회전속도의 4X 성분
P6 기본열주파수
Transition Rule
T1 P1 P4
T2 P2 P4
T3 P2 P5
T4 P3 P4
T5 P3 P6
Source Transition
Source Place
T6 P1
T7 P2
T8 P3
PUKYONG NATIONAL UNIV. Intelligent Mechanics Lab.
Diagnosis of Rotating Machine by Petri Net
Diagnosis ResultsDiagnosis Results
Symptoms Causes Minimal support
T-invariant
회전속도의 많은 조화성분 (P4)과 구별되는 회전속도의 4X성분 (P5)
축상에서 헛도는 베어링의 기계적인 느슨함 (P1)과 하우징 내에서 베어링 느슨함 (P2)
[1010011011]
하우징내에서 베어링 느슨함 (P2) 과 과도틈새 (P3) [0011001111]
원인 추정 하우징 내에서 베어링 느슨함 (P2)
회전속도의 많은 조화성분 (P4)과 기본열주파수 (P6)
축상에서 헛도는 베어링의 기계적인 느슨함 (P1)과 과도틈새 (P3)
[1000110111]
하우징 내에서 베어링 느슨함 (P2) 과 과도틈새 (P
3)[0100101111]
원인 추정 과도틈새 (P3)
PUKYONG NATIONAL UNIV. Intelligent Mechanics Lab.