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A Web-based Intelligent Hybrid System for Fault Diagnosis

A Web-based Intelligent Hybrid System for Fault Diagnosis. Gunjan Jha Research Student Nanyang Technological University Singapore. Presentation Overview. Traditional Hotline Service Support Related Work & Techniques The WebService System Customer Service Database The Hybrid Approach

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A Web-based Intelligent Hybrid System for Fault Diagnosis

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  1. A Web-based Intelligent Hybrid System for Fault Diagnosis Gunjan Jha Research Student Nanyang Technological University Singapore

  2. Presentation Overview • Traditional Hotline Service Support • Related Work & Techniques • The WebService System • Customer Service Database • The Hybrid Approach • Summary & Conclusion 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 AAAI, SSS-'99

  4. Traditional Hotline Service Support 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 AAAI, SSS-'99

  6. 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. AAAI, SSS-'99

  7. The WebService System AAAI, SSS-'99

  8. 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). AAAI, SSS-'99

  9. Intelligent Fault Diagnosis Techniques • Case based reasoning (most popular) • Artificial neural network (Learning Systems) • Rule based reasoning (for Quasi-static systems) • Miscellaneous techniques Fuzzy logic, Genetic algorithms, Decision trees and Statistical techniques • Hybrid techniques AAAI, SSS-'99

  10. 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 AAAI, SSS-'99

  11. Fault Diagnosis Process AAAI, SSS-'99

  12. Knowledge Acquisition AAAI, SSS-'99

  13. Forming a Weight Vector AAAI, SSS-'99

  14. Checkpoint Rule for a Fault-condition AAAI, SSS-'99

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  19. Reuse of Checkpoint Solution AAAI, SSS-'99

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  21. Case Retain or Maintenance Module 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 AAAI, SSS-'99

  23. 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) AAAI, SSS-'99

  24. 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 AAAI, SSS-'99

  25. 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. AAAI, SSS-'99

  26. Performance comparison of three methods to update checkpoint priorities. AAAI, SSS-'99

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