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컴퓨터공학과 98419-531 신수용

Application of Inductive Learning and Case-Based Reasoning for Troubleshooting Industrial Machines - Michel Manago and Eric Auriol. 컴퓨터공학과 98419-531 신수용. Inductive Learning (1/2). Abstract procedure 1. Creates a general description of past examples - create decision tree

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컴퓨터공학과 98419-531 신수용

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  1. Application of Inductive Learning and Case-Based Reasoning for Troubleshooting Industrial Machines- Michel Manago and Eric Auriol 컴퓨터공학과 98419-531 신수용

  2. Inductive Learning (1/2) • Abstract procedure 1. Creates a general description of past examples - create decision tree 2. applies this description to new data • Inductive learning extracts relevant decision knowledge from case history

  3. Inductive Learning (2/2)

  4. Case-Based Reasoning (CBR) (1/2) • Abstract procedure 1. stores past examples - does not requires a tree structure 2. assigns decisions to new data by relating it to past cases • A case • (the description of a problem that has been successfully solved in the past, solutions) • When a new problem is encountered, CBR recalls similar cases and adapts the solutions that worked in the past for the current problem.

  5. CBR (2/2) • Application domain • poorly understood or where rules have many excepts • experience is as valuable as textbook knowledge • CBR makes direct use of past experience • historical cases are views as an asset that should be preserved and it is intuitively clear that remembering pat experience is useful • specialist talk about their domain by giving examples.

  6. Inductive learning vs. CBR • Help-desk areas; troubleshooting complex equipment • performance comparison • pure CBR retrieval is fast for DB with fewer than 10,000 cases

  7. Obtaining better feedback from experiences • CBR and inductive learning help to • improve after-sale support with help-desk software • develop diagnosis and fault analysis decision support system • regularly update troubleshooting manuals from observed faults • capture and reuse the experience of the most talented maintenance specialists • perform experience feedback to increase reliability and maintainability

  8. Applications (1/4) • Decision support system for the technical maintenance of the Cfm56-3 aircraft engines • Combination of inductive and CBR • gather the case data • fault trees have been generated by inductive learning

  9. Application (3/4) • LADI • troubleshoots axis positioning defects • SEPRO Robotique: AcknoSoft installed a CBR help-desk • performs a nearest-neighbor search on the relevant cases

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