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

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Presentation transcript:

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

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

Inductive Learning (2/2)

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.

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.

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

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

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

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