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A Web-based Intelligent Hybrid System for Fault Diagnosis Gunjan Jha Research Student Nanyang...
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Transcript of A Web-based Intelligent Hybrid System for Fault Diagnosis Gunjan Jha Research Student Nanyang...
A Web-based Intelligent Hybrid System for Fault Diagnosis
Gunjan Jha
Research Student
Nanyang Technological University
Singapore
3/23/99 AAAI, SSS-'99 2
Presentation Overview
• Traditional Hotline Service Support• Related Work & Techniques• The WebService System• Customer Service Database• The Hybrid Approach• Summary & Conclusion
3/23/99 AAAI, SSS-'99 3
Traditional Hotline Service Support
• Customers located worldwide make long distance calls to the service centre
• The service engineer provides an advice to the customer by referring to the Customer Service Database (Knowledge Base)
• The service engineer may need to pay an onsite visit if the advice does not work
3/23/99 AAAI, SSS-'99 4
C ustom erservice
database
Searchsystem
G etresu lt
C ustom erServiceEng ineer
Hot-lineadvisorysystem
Telephone line
Advice
Prob lem
Traditional Hotline Service Support
3/23/99 AAAI, SSS-'99 5
Disadvantages of the traditional customer support process
• Expensive overseas telephone calls
• Expensive onsite trips by service engineers
• The need to train and maintain experienced service engineer
• Dependence on the service engineers and the customer service database
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Online Customer Service Support
• BBS (Bulletin Board System)• ARWeb, Cognitive E-Mail, Target WebLink and
ClearExpress WebSupport [Muller 96]• Muller, N.J., 1996. Expanding the Help Desk through
the World Wide Web. Information Systems Management, 13(3): 37-44.
• Compaq, NEC [Chang 1996]• K. H. Chang, et al., 1996. A Self-Improving Helpdesk
Service System Using Case-Based Reasoning Techniques. Computers in Industry, 30(2): 113-25.
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The WebService System
Internet
Web BrowserWeb BrowserWeb Browser Web BrowserUser
Intelligent FaultDiagnosis System
Service Engineer
Intelligent FaultDiagnosis Engine
Web Server
CLIPSRule-base
Neural NetworkIndexing
Database
MaintenanceProgram
Databases
User
CustomerService
Database
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Customer Service Record Database
• A fault record consists of – fault condition – checkpoints
• Example
Fault Condition: CASSETTE DETECTION ERROR.Checkpoints: (1) IS THE CASSETTE 'SITTING' PROPERLY. (2) ENSURE THAT THE TAPE GUIDE IS PROPERLY SET. (3) CONFIRM THE OPTICAL MODULE. (T.G. PG. 10).
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Intelligent Fault Diagnosis Techniques
• Case based reasoning (most popular)
• Artificial neural network (Learning Systems)
• Rule based reasoning (for Quasi-static systems)
• Miscellaneous techniquesFuzzy logic, Genetic algorithms, Decision trees
and Statistical techniques
• Hybrid techniques
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The Hybrid Approach
• Based on hybrid CBR-ANN-RBR approach• Integrate Neural Network into the CBR cycle for
indexing, retrieval and learning• Use Rule-based reasoning for Case-Reuse and
assistance in carrying out the diagnosis• Major tasks:
– Knowledge Acquisition, Retrieval, Reuse, Revise and Retain
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Fault Diagnosis Process
LVQ3neural
network
Revise with user feedback
Knowledgeacquisition
Reuse of service records
Retain by updatingdatabases
CustomerService
Database
Neuralnetworkretrieval
servicerecords
revisedinformation
Problemscenario
description
User retrievedfault-condition
userfeedback
updatedatabases
Pre-processing of
user input
Rule-basedengine
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Knowledge Acquisition
CustomerService
Database
NN Training
NN IndexingDatabase
Rule GenerationNeural Network Training
Pre-processing offault-conditions
Generation ofcontrol rules andcheckpoint rules
Rule-base
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Forming a Weight Vector
Keywords Extracted Index
CUTTER 3ANVIL 1NOT_ENGAGE 8PCB 9
Index Keyword
1 ANVIL 2 BREAK 3 CUTTER 4 CAMERA 5 FAULTY 6 GUIDE 7 NC 8 NOT_ENGAGE 9 PCB 10 SHAKY
CUTTER & ANVIL CANNOT ENGAGE IN AFTER 1ST PCB.
Pre-processing
List of Keywords
.75 00 0 000
Fault-condition
keywordindex
Weight Vector
.75.75.75
3/23/99 AAAI, SSS-'99 14
Checkpoint Rule for a Fault-condition
(defrule MAIN::chkpt_rule-7
(phase accept.fault.cond)
(fault.cond AVF_CHK007)
=>
(assert (check-seq AVF_CHK007-1 AVF_CHK007-2 AVF_CHK007-3
AVF_CHK007-4 AVF_CHK007-5))
(assert (help-seq AVF_CHK007-1.GIF AVF_CHK007-2.GIF
AVF_CHK007-3.GIF AVF_CHK007-4.GIF AVF_CHK007-5.GIF))
)
3/23/99 AAAI, SSS-'99 15
Unknown Keyword Synonym Index
STANDSTILL STAY 12
THE CARRIER DID NOT TRANSFER THE PCB DURING LOADING.
Keywords Extracted Index
CARRIER 2DETECT 3LOAD 5NOT_TRANSFER 7PCB 8SENSOR 11
PCB
PCB DETECTOR SENSOR
STANDSTILL
0 00 01111111 000
Input Vector
keywordindex
User Input
Index K eyword
1 ANVIL 2 CARRIER 3 DETECT 4 DRIVE 5 LOAD 6 NOT_INSERT 7 NOT_TRANSFER 8 PCB 9 PUMP10 RAIL11 SENSOR12 STAY13 TRANSFORMER
Pre-processing
keyword index
User
List of keywords
Feedback onSynonym
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Reuse of Checkpoint Solution
CustomerService
Database(Unique Set)
Rule-basedInference
Engine
CheckpointsolutionRetrieved
fault-condition
User
Feedback
Rule-base(Checkpoint
Rules)
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User's feedback
Checkpointpriority
modification
SuccessfulCorrect fault-condition
and checkpointFailure
Checkpoint solutions fail
CustomerService
DatabaseWeights update
Update input vector
Update keyword list
Update fault-conditions to
keyword-list index
NN indexingdatabase update
Rule-basegeneration
Rule-baseNN
IndexingDatabase
Fault diagnosisthrough serviceengineer's help
service report
Pre-processigand training
Update servicerecord
Maintenanceprogram
Serviceengineer
Rule-base update
servicerecord
3/23/99 AAAI, SSS-'99 22
Summary
• The research has successfully demonstrated the effectiveness of hybrid CBR-ANN-RBR approach for the fault diagnosis problem
• Performance analysis have proved the approach to be much accurate and efficient than the traditional CBR techniques (Nearest Neighbor)
• Future work focuses on incorporating genetic algorithm and data mining techniques for better accuracy and efficiency
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Performance Analysis
• Performance compared with traditional CBR systems using kNN technique
• Retrieval Accuracy: test data from customer
service database (size ~ 15000)– ANN: 93.2%– kNN1: 76.7% (Fuzzy Trigram)– kNN2: 81.4% (Euclidean distance based matching)
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Performance Analysis (…continued)
• Retrieval Accuracy: test data from the user input (size = 50)– ANN: 88%– kNN1: 78% (Fuzzy Trigram)– kNN2: 72% (Euclidean distance based matching)
• Average Retrieval Speed (test size ~ 15000)– ANN: 1.9s– kNN1: 12.3s – kNN2: 9.6s
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3 possible methods to Update Checkpoint-Rule Priority
• Method 1: No need to change the priorities of checkpoints.
• Method 2: Assign priority “1” to the checkpoint that solves the problem and decrease the priorities of the checkpoints ahead of it by “1”.
• Method 3: Swap the priority of the checkpoint that solves the problem with the one just ahead of it.