회전체

Recent Research Activities InIntelligent Mechanics Laboratory

http://vibration.pknu.ac.kr

Professor Bo-Suk Yang

연구분야 : 회전기계의 진동해석 및 최적설계

회전기계의 상태감시 및 이상진단

학 력 : 1978. 부경대학교/기관공학/ 공학사

1980. 부경대학교/ 진동공학/ 공학석사

1985. 일본 Kobe대학/ 진동공학/ 공학박사

수상경력 : 1987. 일본기계학회상(장려상)

1987. H.H. Jeffcott Award/ 미국기계학회 우수논문상

1994. 과학기술우수논문상/ 한국과학기술단체 총연합회

1995. 최우수논문상/ 한국박용기관학회

2002. 강월논문상/ 한국소음진동공학회

2003. 부경학술상/부경대학교

1999~ : Who’s Who In The World (세계인명사전) 등재

1999 : Five Hundred Leaders of Influence 수상

연구분야 : 회전기계의 진동해석 및 최적설계

회전기계의 상태감시 및 이상진단

학 력 : 1978. 부경대학교/기관공학/ 공학사

1980. 부경대학교/ 진동공학/ 공학석사

1985. 일본 Kobe대학/ 진동공학/ 공학박사

수상경력 : 1987. 일본기계학회상(장려상)

1987. H.H. Jeffcott Award/ 미국기계학회 우수논문상

1994. 과학기술우수논문상/ 한국과학기술단체 총연합회

1995. 최우수논문상/ 한국박용기관학회

2002. 강월논문상/ 한국소음진동공학회

2003. 부경학술상/부경대학교

1999~ : Who’s Who In The World (세계인명사전) 등재

1999 : Five Hundred Leaders of Influence 수상

PUKYONG NATIONAL UNIV. Intelligent Mechanics Lab.

FacultyProf. Bo-Suk Yang, Ph.D.

FacultyProf. Bo-Suk Yang, Ph.D.

Alumnus(1986 ~2004)Alumnus(1986 ~2004)

Registered Students :Ph.D. : 10

Young-Mo Kong, Soo-Mok Lee

Jin-Dae Song, Chang-Mook Lee,

Jun-Ho Park, Byoung-Wook Kim,Tian-Han, Byoung-Rae Kil,

Chul-Hyun Park,

Jong-Yong Ha, Achmad Widodo

M.S. : 7Jong-Duk Son, Jae-Gap Lee,

Di Xiao, Niu Gang

Dong-Ho Yoon, Byung-yun Moon, Hyun-Chul Kim

Undergraduate : 5

Registered Students :Ph.D. : 10

Young-Mo Kong, Soo-Mok Lee

Jin-Dae Song, Chang-Mook Lee,

Jun-Ho Park, Byoung-Wook Kim,Tian-Han, Byoung-Rae Kil,

Chul-Hyun Park,

Jong-Yong Ha, Achmad Widodo

M.S. : 7Jong-Duk Son, Jae-Gap Lee,

Di Xiao, Niu Gang

Dong-Ho Yoon, Byung-yun Moon, Hyun-Chul Kim

Undergraduate : 5

Faculty & Student


Ph.D. M.S. B.Eng.

10 54 3

Publications

Domestic Journal Papers 85

Domestic Proceedings 119

International Journal Papers 42

International Conference Proceedings 71

Technical Reports 28

Technical Books 11

Total (1985 – 2003)

2003Domestic Journal Papers 6

Domestic Proceedings 13

International Journal Papers 12

International Conference Proceedings 12


Research Object

Steam Turbine Generator

Motor/Generator

Fan/Blower

Gas Turbine

Compressor

Pump

Rotating machinery

Turbocharger


Rotating Machinery( Pump, Motor, Turbine, Generator, Compressor, etc.)

Rotating Machinery( Pump, Motor, Turbine, Generator, Compressor, etc.)

Dynamic Optimum Design Monitoring & Diagnostics

Vibration Analysis

Research Area


Applications:Horizontal and Vertical PumpsSteam Turbine/Generator System for Thermal & Nuclear Power PlantSmall Fan/DC Motor Rotor System for RefrigeratorGeared & Coupled System (Bending & Torsional Vibration)Turbocharger Rotor System for Marine Diesel EngineDevelopment of General Purpose Software [EZ-ROTOR]

Applications:Applications:Horizontal and Vertical PumpsSteam Turbine/Generator System for Thermal & Nuclear Power PlantSmall Fan/DC Motor Rotor System for RefrigeratorGeared & Coupled System (Bending & Torsional Vibration)Turbocharger Rotor System for Marine Diesel EngineDevelopment of General Purpose Software [EZ-ROTOR]

Vibration Analysis

Topics:Hydrodynamic Analysis of Fluid Film BearingElectromagnetic Force Analysis of MotorMount or Squeeze Film Damper using MR FluidSeal Dynamics Finite Element Method, Transfer Matrix Method

Topics:Topics:Hydrodynamic Analysis of Fluid Film BearingElectromagnetic Force Analysis of MotorMount or Squeeze Film Damper using MR FluidSeal Dynamics Finite Element Method, Transfer Matrix Method


Methods & Tools:Random Tabu Search MethodGenetic AlgorithmArtificial Life AlgorithmSimulated AnnealingHybrid Evolutional Algorithm

Methods & Tools:Methods & Tools:Random Tabu Search MethodGenetic AlgorithmArtificial Life AlgorithmSimulated AnnealingHybrid Evolutional Algorithm

Applications :Optimum Shape Design of Steam Turbine, Motor, CompressorOptimum Design for Bearing & Seal GeometryOptimum Design for Engine Mount & Squeeze Film DamperOptimum Layout of Damping Material Optimum Allocation of Piping SystemModel Updating & Parameter Identification

Applications :Applications :Optimum Shape Design of Steam Turbine, Motor, CompressorOptimum Design for Bearing & Seal GeometryOptimum Design for Engine Mount & Squeeze Film DamperOptimum Layout of Damping Material Optimum Allocation of Piping SystemModel Updating & Parameter Identification

Dynamic Optimum Design


Development of Vibration Diagnostics AlgorithmsArtificial Neural Networks ( SOFM, LVQ, RBF, ART etc)Support Vector MachinesDecision Tree & Table, Fault Tree MethodExpert SystemPetri Net Technique

Case-Based Reasoning & DiagnosticsConstruction of Case Base by Vibration TroubleshootingWeb Site of Case Base Search(http://vibration.pknu.ac.kr)

Wavelet Analysis & Feature Extraction TechniquesDWA, Statistics

Principal Component Analysis, Genetic Algorithm

Development of Vibration Diagnostics AlgorithmsDevelopment of Vibration Diagnostics AlgorithmsArtificial Neural Networks ( SOFM, LVQ, RBF, ART etc)Support Vector MachinesDecision Tree & Table, Fault Tree MethodExpert SystemPetri Net Technique

CaseCase--Based Reasoning & DiagnosticsBased Reasoning & DiagnosticsConstruction of Case Base by Vibration TroubleshootingWeb Site of Case Base Search(http://vibration.pknu.ac.kr)

Wavelet Analysis & Feature Extraction TechniquesWavelet Analysis & Feature Extraction TechniquesDWA, Statistics

Principal Component Analysis, Genetic Algorithm

Condition Monitoring & Diagnostics


Vibration Analysis

HP Rotor LP Rotor

Turbine/Generator Rotor System

Steam Turbine-Generator Rotor System(KORI #3,4, 1007MW)


Damping and Stiffness Coefficients of No. 2 Bearing




0 500 1000 1500 20001E-5

1E-4

1E-3

0.01

0.1

1

#5 bearing

#9 bearing

Am

plitu

de (

mm

)

Rotating speed (rpm)


Pump\Turbine-Generator\Motor Rotor Model(Muju #1, 2, 336MW)


Pump\Turbine-Generator\Motor Rotor Model(Muju #1, 2, 336MW)

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

plitu

de (µ

m)


100 200 300 400 500 600 700 800

0.01

0.1

1

10

Operating speed

Generator/motor

Runner

Am

plitu

de (µ

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

ural

freq

uenc

y (c

pm)


Hydraulic unbalance

Mechanical unbalanceMode shape

Add-mass effect of water


Finite Element Modeling

Specifications of rotor-shaftρ 7850 kg/m3

E 205 Gpa

l 482 mm

D 0.045 mm

m 13.2 kg

Ip 0.11207 kgm2

Id 0.07844 kgm2

m 32.773 kg

Ip 0.16176 kgm2

Id 0.16089 kgm2

Turb.wheel

Comp. wheel

Shaft

28000 rpmRated Speed

3.83Co/Ci

0.51L/D

0.01831 Pa⋅s/m2Oil Viscosity

Bearing parameters

Turbocharger Shaft


Turbocharger Shaft

Modeling

Floating Ring Journal Bearing

Stodola mention FJB in 1927 firstlyTwo fluid film with inner and outer films

High damping propertyApplication to high speed turbines, excessive bearing temperature

Effective in suppressing both resonantvibrations at critical speed and self-excited vibration called oil whip

Dynamic analysis method- Impedence description- Finite difference method(FDM)


Turbocharger Shaft

Damping ratio diagram

Unstable

Stable

Two modes (a, b) are estimated as unstable modesStable threshold speed : 10600 rpmBearing design have to be modified


CControllable ontrollable Squeeze Film Damper with MR FluidSqueeze Film Damper with MR Fluid

MR FluidMR Fluid

ShaftShaft

Magnetic Field Magnetic Field DirectionDirection

Ball BearingBall Bearing

Controllable SFD with MR FluidControllable SFD with MR Fluid

Flexible ShaftFlexible Shaft Flexible CouplingFlexible Coupling

Fly WheelFly Wheel

Displacement SensorDisplacement Sensor

Motor

Conventional SFDConventional SFD


Optimization Algorithms &Optimum Design

Evolutionary Algorithms

• Genetic Algorithm (GA) :자연계의 생명체 중 환경에 잘 적응한 개체가 좀더 많은 자손을 남길

수 있다는 자연선택 과정과, 자연계의 생명체의 설계도와 같은

유전자의 변화를 통해서 좋은 방향으로 발전해 나간다는 자연진화의

과정을 모방하여 컴퓨터로 모의 수행을 하는 최적화 알고리즘.

• Artificial Life Algorithm (ALA) : 자연생명계의 살아있는 시스템이 나타내는 거동을 모의하는

인공시스템을 응용한 알고리듬.

• Simulated Annealing (SA) :금속의 냉각과정에서의 조직 안정화현상을 모사한 최적화 알고리듬.

• Genetic Algorithm (GA) :자연계의 생명체 중 환경에 잘 적응한 개체가 좀더 많은 자손을 남길

수 있다는 자연선택 과정과, 자연계의 생명체의 설계도와 같은

유전자의 변화를 통해서 좋은 방향으로 발전해 나간다는 자연진화의

과정을 모방하여 컴퓨터로 모의 수행을 하는 최적화 알고리즘.

• Artificial Life Algorithm (ALA) : 자연생명계의 살아있는 시스템이 나타내는 거동을 모의하는

인공시스템을 응용한 알고리듬.

• Simulated Annealing (SA) :금속의 냉각과정에서의 조직 안정화현상을 모사한 최적화 알고리듬.


StartStart

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


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 suppressor cellsAffinity ≥ Tacl

Calculation of the affinity between individuals and suppressor cellsAffinity ≥ Tacl

Proliferation andSuppression of

individuals

Proliferation andSuppression of

individuals

Immune Genetic Algorithm(IGA)



CoreFan

Bearing Bearing

Object function :

0

2

0

2

0 Q)() ()(

)(xxx

xQ

WW

F F ++=ω

ω


min.

IGAInitial

value Weight, W Bearing force, bF3

Critical speed, cnω (rad/s)

c2ω 685.84 538.34573 549.86c3ω 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, (µm)

MaxAmp 78.9 65.87 68.9

(b) Original model

(a) Optimum model



Enhanced Genetic Algorithm (EGA)

StartStart



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 = 1FAC = 1

Yes

No

Affinity < 0.1Affinity < 0.1 Erasion candidacy solution

Erasion candidacy solution

No

Yes

Global search



Simplex methodSimplex method

Local search

No

Yes

Affinity < 0.1Affinity < 0.1

EndEnd



HP Turbine

LPATurbine

LPBTurbine Generator

00

0

0

)()()()(Q

xQxW

xWxf ii γω

ωωβα +

−+= min.

Object function :



(a) Original shaft model (b) Optimum shaft model

Objective function Original design

Optimum design

Total weight, W (x) 1427.88 kN 1425.84 kN

Q factor, Q1 (x) 56.1396 48.710

1st natural frequency, 894.8 cpm 892.9 cpm

Optimization Results


Enhanced Artificial Life Algorithm (EAL)

Random Tabu Search Method

x0 : initial candidate solutionN(x0, h1) : neighbor region

Step size : number of sub-stepsCount number : maximum number of trial solution

to search in one region

Flow chart for proposed algorithm

Start

Metabolism using R-TabuMovement using R-Tabu

Initial distribution

Search resource

END

Age = Age +1Gen = Gen + 1

Gen > Gen_max

Make offspring using R-Tabu

Characteristics of proposed method:searching the various regions in a size


Simple rotor supported by hydrodynamic journal bearing

Object function : minimize Q factor



0.25 0.3 0.35 0.4

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5x 10-4C

lear

ance

C(m

)

L/D

Optimum

Feasible solutionregion

Original



Clustering based Hybrid Evolutionary Algorithm (CHEA)

START

END

GA-LS

Random search

GA-Clustering

Local Search for each cluster

Converge to one point ?

For j th cluster Return

Local Search from several point

Converge to one point ?

Reduce bounds as this cluster ’s

Function GA-LS

Local minimum found

Return

Local minimum found

N

N

Y

Y

Recursive call GA -LS

Initial population

Fitness evaluation

CEF < stop_criterion ? Return

Function GA-Clustering

N

Y

Clustering by using ART-KNN

Parents selection

Crossover, Mutation

New population


Clustering based Hybrid Evolutionary Algorithm (CHEA)

Bearing parameter identification

500 1000 1500 200010

0

101

102

103

Rotating s peed (rpm)

Response (µm)

Me as ure dIde ntifie d


Condition Monitoring &Diagnostics

Integrated Classification System for Diagnostics


A/D ConversionA/D Conversion

Hardware

Transient Stable

Software

Signal Data, Condition, Specification,Date, Sensor Information

Database

W/T TransformFeature ExtractionW/T Transform

Feature Extraction

Training (Neural Network) :SOFM, LVQ

Training (Neural Network) :SOFM, LVQW/T Transform

Feature ExtractionW/T Transform

Feature Extraction

Trained DataCondition ClassificationCondition Classification

Software Process

Software


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

Integrated Classification System for Diagnostics

Data Input

Data ManagementClassification


Integrated Classification System (RECODIS)

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

Case-Based Reasoning System


http://vibration.pknu.ac.kr

Case-Based Reasoning System


Funded Research Project

Intelligent Mechanics Lab.PUKYONG NATIONAL UNIV.


Funded Research Project

진동을 이용한 유도전동기 감시 및 진단 알고리즘 개발 2002-2004 Poscon

함정 소음원 진단 시스템 개발 2000-2005 ADD

특수 증기터빈용 베어링 설계 및 축계 진동 해석 기술 개발 2003-2004 두산중공업

중대형 고성능 과급기(Turbocharger) 개발 2001-2005 STX

증기터빈용 틸팅 패드 베어링 국산화 개발 2004-2006 한국전력연구원

영구 자석형 직류전동기의 진동특성 진단 및 진동 해석 2004-2006 대우정밀

전동기 이상진동 진단 프로그램 개발 2004-2006 현대중공업

전체 수행과제 (1986~ 현재) : 38개

수행 중 과제 :


Noise Source Identification of Fan-Motor

BLDC motor

Axial fan

Motor cover

Shroud

BLDC motor

Axial fan

Motor cover

Shroud

Rated voltageRated speedFan diameterShaft length

9-15 V1200-1310 rpm

145 mm110 mm

Freezer chamber

Coolchamber

Comp Cond

Condenser cooling fan

Cold-air circulation fan



268 320 344 420

Measure

(ETET)

Torsional

418

Analysis

(FEA)

In-phase Bending Hub

In-phase Tilting

Torsional

323314258 407




유도전동기 감시 및 진단 알고리듬 개발

Induction Motor

Data Acqusition

Feature Extraction(Wavelet Transform, Statistics)

Feature Selection

ART KNNNeural Network

Fault Identification

Training

Classification

지원 기관 : Poscon(주)

연구 내용 및 목표

- 진동 신호를 이용한 통계적 특징 추출기법 개발

- 인공 신경망(ART-KNN)을 이용한 진단

알고리듬 개발

- 상태 감시용 신호처리 및 분석기법 연구

(시간영역, 주파수영역, Wavelet 분석)



Test rig

Speedometer

Accelerometer

AC current probes



Unbalance mass

Rotor unbalance53 54 55 56 57 58 59 60 61 62 63

0.0000

0.0002

0.0004

0.0006

0.0008

0.0010

0.0012

0.0014

0.0016

0.0018

0.0020

Unbalance Noraml

Am

plitu

de

Time (s)

Rotating frequency (Hz)



Bowed rotor Misalignment



Full-load condition

Normal condition Parallel eccentricity Angle eccentricity



Upper-bound & Entropy error Kurtosis & skewness

Feature parameters: Mean, RMS, Shape factor, Skewness, Kurtosis, Crest factorRMSF, FC, RVF, Entropy Estimations, Entropy error, Histogram (upper-bound and lower-bound)

Full-load condition

최적화 기법을 이용한 Containership 저진동 최적설계

Dimension(M) , LBP×B ×D×d (196.0×29.8×16.4×10.2)Loading case : Ballast, 19478 ton, Engine type : 6RTA72U Engine (97rpm, 17940 kW)Propeller : 5 blade기진력 : H-moment, X-moment, Propeller부가수 질량 고려

감쇠 ( 0.0Hz 0.0%, 20Hz 6.0% )



OptShip

NASTRANCall one Optimization

method

Update model

Read results

Estimate F(X*)

AppUmodel

Wmodel

ExNas

Rforced

Rmode

Estimation

Initialize

OptRtabu

GA

…

Init

END

Print

START

Structure of the OptShip



D/H longi. mode coupled with Aft. bodyand double bottom

Frequency : 8.36 Hz

D/H torsional mode coupled with Aft. body

Frequency : 8.41 Hz

D/H torsional mode coupled with H/G

Frequency : 7.00 Hz



50 60 70 80 90 100 1100

5

10

15

Vel

ocity

[mm

/s]

Navi.De ck(P ort, Longi.), Mag

OptshipNas /OptOrigina l

50 60 70 80 90 100 1100

5

10

15

Vel

ocity

[mm

/s]

Navi.De ck(P ort, Trans .), Mag

50 60 70 80 90 100 1100

5

10

15

Vel

ocity

[mm

/s]

R.P .M.

Navi.De ck(P ort, Ve rti.), Mag


Thank You

Documents

회전체