1. Bahar Qarabaqi Azar 19 th, 1386

2. FC Inferencing Initial information about the problem being asserted into working memory. Database Sensors User

3. FC Inferencing (cont.) 1. Scan the rules looking for ones whose premises match the contents of the working memory. 2. Fire the rule which was found. 3. Place its conclusion in the working memory. 4. Until no additional rule fire, go to 1.

4. 2. Fire the rule which was found… May locate several rulesmust decide Recognize-Resolve-Act cycle: 1.Scan the rules looking for ones whose premises match the contents of the working memory. 1’.Choose one rule to fire. 2.Fire the rule which was found. 3.Place its conclusion in the working memory. 4.Until no additional rule fires, go to 1.

5. 1’. Choose one rule to fire… Conflict resolution Simplest: Rules are examined in order The first rule is chosen A common strategy: Each rule has a number which indicates its priority The rule with the highest priority is chosen …

6. Example 1: Pumping Station Diagnostic System Block: a pump & a motor Increase the water pressure by 50 psi Sensors: line pressure motor currents Nominal values are available An Event-Driven ES

7. Event-Driven vs. Conventional ES Conventional ES interacts with a user. Event-Driven ES only becomes active when some special event occurs

8. Example 1–Problem Solving Approach Problem solving strategy: Fault detection Fault isolation Fault diagnosis Most ESs follow this sequence. Some also include fault response. Knowledge base is divided into various sections

9. Example 1 – Fault Detection 1. Numeric readings qualitative descriptions 2. Any faultlow pressure 3. Faults propagate Result: only need to monitor the final line pressure

10. Example 1 – Fault Detection (cont.)

12. Example 1 – Fault Isolation Why isolation? By first identifying the faulty block, the system can concentrate its diagnostic effort on this single block. Comparison of the block’s input pressure with its output pressure

13. Example 1 – Fault Diagnosis Which component is at fault? Motor: low current Pump: no change in pressure Line: the block’s input pressure is less than its output pressure

14. Example 1 – Fault Diagnosis (cont.)

15. Example 1 – Review Partitioned Rules: Improve readability enhance maintenance Intermediate Findings: Reports: what was observed and what the system would look into next Intelligent Safety Net: If the system is unable to determine the faulted component, knowing the general source may be valuable.

16. Example 1 – Review (cont.) Numeric Relationships: 49.99 is a low pressure! Solution: fuzzy logic Specific Rules: Similar but separate rules for similar objects Solution: Example 2! specific objects as variables

17. Example 2: Generalized Pumping Station Diagnostic System Information about the structural relationship between components

18. Example 2 – Problem Solving Approach Problem solving strategy: General Fault Detection General Fault Isolation General Fault Diagnosis General Fault Response

19. Example 2 – General Fault Detection Fault Detection Heuristic: If any line pressure drops below its nominal pressure, then you have a fault condition. Used in RULE 1S

20. Example 2 – General Fault Detection

21. Example 2 – General Fault Isolation Fault Isolation Heuristic: If you notice that a block’s input pressure is normal, but its output pressure is low, then the block may be faulty.

22. Example 2 – General Fault Diagnosis Which component is at fault? Motor Problem Heuristic: A motor with a low current is suspect. Pump Problem Heuristic: Pump problems usually result in no pressure changes across the pump. Line Problem Heuristic: When you see no problems with a block’s motor, but there is some increase in pressure across the block, there may be a leak in the output line.

23. Example 2 – General Fault Diagnosis

24. Example 2 – General Fault Response Purpose: replace the faulty component Only when the user has granted permission

25. Example 2 – Review Streamlining Rules: A small number of general rules containing variables instead of a large set of rules. Ease of Expansion: If additional objects added, only need to assert their initial configuration information Stopping the System: 1. A common technique, but not the best: on their own 2. Force the system to stop

26. Example 2 – Review (cont.) Requesting Information: 1. Startup Rule: IFGet initial information ThenASK … ANDASK … 2. When certain events occur 1. As a part of a rule (our example) 2. Demon Rule: the highest priority IF?Line pressure-status low THENASK shut down

27. Example 3: Train-Loading Expert System Problem: Pack the passengers of various weights into a series of train cars. Pack the persons by decreasing weight. Not exceed maximum weight capacity of train car. Maximize the number of persons per train car thus minimizing the number of cars needed.

28. ESs for Design Applications Design: configuring objects under a set of constraints Constraint: 1. Requirements: meet the design goal FC required: BC does not work in design applications. 2. Methods: order of the steps 1. Control Rules required: IF AND THEN 2. Rule Ordering: simple to design, difficult to maintain 3. Rule Priorities: difficult to maintain

29. Example 3–Problem Solving Approach

32. Example 3– Review Designed to Spec: Final design met the stated specifications! Rules in Design Systems: IF AND THEN