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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
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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 Tel) 82-51-620-1604, 625-1604 Fax)+ 82-51-620-1405 E-mail) dmlab@dolphin.pknu.ac.kr Home page: http://vibration.pknu.ac.kr
Faculty & Student • 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 • Alumnus(1986 ~2000)
Dynamic Optimum Design Vibration Analysis Monitoring & Diagnostics Research Area Rotating Machinery (Pump,Motor,Turbine Generator, Compressor, etc.)
Research Area • 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
Dynamic Optimum Design • 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
Vibration Analysis • SoftwareDevelopment 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)
Condition Monitoring & Diagnostics • 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 ofCase Base by Vibration Troubleshooting • Web Site of Case Base Search(http://vibration.pknu.ac.kr) • Wavelet Analysis & Feature Extraction • Ball Bearing Defect, Rubbing
Vibration Analysis of Turbine-Generator System for Nuclear Power Plant
Steam Turbine-Generator Shaft Model (Kori #3 & 4, 1007MW) Turbine/Generator Rotor System HP Rotor LP Rotor
Vibration Analysis : Bearing Dynamic Coefficients Damping and Stiffness Coefficients of No. 2 Bearing
Vibration Analysis : Comparison of Natural Frequency & Error
Vibration Analysis : Unbalance Response Comparison of unbalance response at bearing No.5, 9
Vibration Analysis of Turbine-Generator System for Pumped-Storage Power Plant
Pump\Turbine-Generator\Motor Shaft Model (Muju #1, 2, 336MW)
Vibration Analysis : Mode Shape & Unbalance Response Mode shape Mechanical unbalance Hydraulic unbalance Add-mass effect of water
Analytical Model & Earthquake Wave Turbine-generator model and KOBE earthquake wave(EW)
Seismic Response : Comparison of Analysis Methods Direct integration method Modal superposition method
Seismic Response Analysis : Bending Stress Distribution of bending stress at maximum displacement position
Seismic Response Analysis: Wavelet Transform Wavelet transform of seismic response wave at bearing No.9 EW component UD component
Start Production of the initial chromosome calculation of the fitness The differentiation of memory and suppressor cell Calculation of the fitness and affinity of individuals Calculation of the affinity between individuals and suppressor cells Affinity Tacl Production of the individuals Selection Proliferation and Suppression of individuals Crossover and Mutation Change old population with new population No Gen Max.Gen Yes End Flow Chart forImmune Genetic Algorithm (IGA)
(b) Original model Optimization Result of IGA (a) Optimum model
Start Calculation of the affinity between saved candidacy solution set Production of the initial chromosome calculation of the fitness Erasion candidacy solution Affinity < 0.1 No Calculation of the fitness Yes FAC = 1 Decision and reallocation of candidacy solution Yes Selection next solution No Yes Fmin = Fmax No Production of the individuals Solution num. = N No Selection No Production of the individuals Yes Crossover and Mutation Affinity < 0.1 Selection and Crossover Change old population with new population Change old population with new population Yes End Global search Local search Flow Chart forEnhanced Genetic Algorithm
Characteristics of Artificial life Algorithm Circular food chain Dynamic interaction in the environment
Flow chart for Artificial Life Algorithm Step 1Initialization Step 2Search resource Step 3Movement using elite reservation strategy Step 4Metabolism Step 5Increasing age Step 6Reproduction Step 7Reducing energy Step 8Increasing generation
= 0 = (1.0, 1.0) Optimization result of Artificial life Algorithm Emergent Colonization produced at the optimum point Contour line and emergent colonization for banana function
= {(0.0898, -0.7126), (-0.0898, 0.7126)} = -1.0316 Optimization result of Artificial life Algorithm Emergent Colonization produced at the optimum point Contour line and emergent colonization for camel function
Structure of Classification System for Diagnostics 1. Experimental Configuration 2. A/D Converting System 3. Database Management Database Storage Module 1. Wavelet Transform 2. Statistical Feature Extraction 3. Training using Neural Network Data Training Module 1. Untrained New Data 2. Store to Database Classification Module
Integrated Classification System for Diagnostics Hardware Software Signal Data, Condition, Specification,Date, Sensor Information A/D Conversion Transient Stable Database Software Process W/T TransformFeature Extraction Software Training (Neural Network) :SOFM, LVQ W/T TransformFeature Extraction Trained Data Condition Classification
Wavelet Transform Normal Abnormal Statistical Evaluation Value :Mean, Standard deviation, Skewness, Kurtosis More & Robust Features than Time-waveform
Neural Network Classification Self-Organizing Feature Map & Learning Vector Quantization Technique Training Data Trained Data Re-organized into CODEBOOK Vectors Which Class? k-NN Technique Untrained Data
Introduction of Case-Based Reasoning System - Memorize previous situation and case-history - Reuse to for solving new problem - Previous problem solving current problem solving
CBR System for Vibration Diagnosis • Keywords and Weights : Extracted from the Case-Base and Stored to Library • Categories and Details : Can be Added through CBR Cycle
Input and Output of CBR System http://vibration.pknu.ac.kr
Petri Net Algorithm for Abnormal Diagnosis Occurrence of Abnormal Vibration at Rotating Machine Background Expression of Symptom Freq. by Target Transition Cause Diagnosis by Symptom Frequency Petri Net Carl A. Petri 1962 Minimal Support T-invariant Calculation Addition of Source Transition at All Source Places PUKYONG NATIONAL UNIV. Intelligent Mechanics Lab.
Diagnosis of Rotating Machine by Petri Net Modeling PUKYONG NATIONAL UNIV. Intelligent Mechanics Lab.
Diagnosis of Rotating Machine by Petri Net Diagnosis Results PUKYONG NATIONAL UNIV. Intelligent Mechanics Lab.