tailieunhanh - Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design - Part 78

Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design - Part 78 studies the combination of various methods of designing for reliability, availability, maintainability and safety, as well as the latest techniques in probability and possibility modelling, mathematical algorithmic modelling, evolutionary algorithmic modelling, symbolic logic modelling, artificial intelligence modelling, and object-oriented computer modelling, in a logically structured approach to determining the integrity of engineering design. . | 754 5 Safety and Risk in Engineering Design PRO RAM RELIABILITY AVAILABILITY MAINTAINABILITY BLACKBOARD FOR ENGINEERING DESIGN REVIEW NU- Aspfcaton Site-wife Specdcrfiom Overview Syxtenv. LHc PFDMofefe DateBtow Hetwork Uh NcuralExpeil - Step 11 o 3 Generakzabon Protection 9 Out of Sarrple Testing 10 Genetic Optmeation n Set Network _ Complexity Help Cancel gack fjrxt Rrwsh Choose a level of neural network complexity. Simple networks will Iran faster and produce better results if they are sufficiently powerful to solve the problem. Since most people underestimate the power of neural networks we suggest that you start with Tow complexity and increase if per fcr mace is poor. C Low r Ugh Fig. ANN NeuralExpert network complexity PFD of a plant to access specific details of any object shown on the PFD as well as the object s detailed specifications diagnostics or performance measures. The diagnostics inference engine contains diagnostic charts and queries relating to failure characteristics failure conditions equipment criticality performance measures and operating and maintenance strategies. The knowledge base consists of facts and functions relating to all the technical data pertaining to process definition systems definition performance assessment and analysis conditions and constraints relating to equipment failure modes and effects the level of risk and mitigating maintenance procedures as well as an assessment of the required resources. Figure illustrates the AIB blackboard knowledge base user interface to access the various expert systems with their rules and goals. A knowledge-based expert system emulates the interaction a group of multidiscipline design engineers will have in solving a design problem. The decision trees or rules used in a knowledge-based expert system contain the knowledge of the human specialist s in a particular field. The inference engine makes use of these rules to solve a problem in achieving set goals design criteria . The end

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