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Attribute List: CA provides public attributes shown in the Table 1.
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Main Controller: It is the core part, which transfers every device and resource to response the external environmental change.
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State Device: It is the equipment for Agent to control internal state, causes the internal state to transfer and change according to Main Controller, and delivers the change to external environment.
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Environment Effector: It is dedicated to adjust automatically failure rule of component, in control of Main controller, according to external change message, by renewing failure generating algorithm.
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Communication Device: It is communications-equipment between Agent and external environment, and a main channel that apperceives and influences external environmental change.
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Failure Generator: It is made up of randomizer and failure sampling algorithm, etc, and special to simulate function failure of component.
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Reliability Relationship Controller: It is an internal device, which is specially devised for materiel system reliability simulation in the paper. The function is to control dynamically the reliability relationship among Agents, makes up a system reliability test channel, with R Upstream Port and R Downstream Port, and cooperates with subordinate system to carry out state detection. Table1 The Public Attributes of Adaptive Agent Attribute Name
Attribute Type
Type of Value
Description
Number
Basic Information
String
Identification Code of Simulation Entity.
Name
String
Component Name.
Type of Failure Distributing
Failure Rule
Integer
The kinds of type of Failure Distributing are 29.
Distributing Parameter 1
Double
The first parameter of failure distributing.
Distributing Parameter 2
Double
The second parameter of failure distributing.
Distributing Parameter 3
Double
The third parameter of failure distributing.
Distributing Parameter 4
Double
The fourth parameter of failure distributing.
work pattern
Operation Parameters
Integer
It is the mode of Agent action in the course of simulation, including: normal, continuous and hand-actuated work pattern.
Mean Time To Repair
Double
It is mainly in consideration of maintenance situation for component.
Structure Factors
Environmental Effect Factors
Double
The variable that denotes structure effect factors.
Temperature Factors
Double
The variable that denotes temperature effect factors.
Humidity Factors
Double
The variable that denotes humidity effect factors.
Height Above Sea Level Factors
Double
The variable that denotes height above sea level effect factors.
Usage Factors
Double
The variable that denotes usage intensity effect factors.
Voltage Factors
Double
The variable that denotes input voltage effect factors for electronic products.
Atmospherical Environment Factors
Double
The variable that denotes environmental effect factors, for example fine sand flying up in the air，salt mist，acid, alkaline and caustic atmosphere, etc.
Quality Factors
Double
The variable that effects quality, for example the choose of parts and the level of quality, etc.
Magnetic Effect Factors
Double
The variable that denotes magnetic field effect factors.
Other Effect Factors
Double
The variable that denotes other related effect factors.
Subordinate System
Affiliation
SA
The object of subordinate system Agent for component.
Function Model The function model of CA is defined by IDEF0 language shown in Fig.3. Fig.3. The System Function Model of Environment Adaptive Agent
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A0:Communication Device accepts the message from external environment, and according to the arriving time of the message, puts the message into Message Queue to wait for be processed.
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A1:Message Decoder in Communication Device analyzes the message in Message Queue, decodes and transmits the message to Main Controller.
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A2:After accepting the change information about reliability model, according to the new information, Main Controller rebuilds reliability relationship among other Agents.
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A3:After accepting the change information about environment parameter, Main Controller renews environmental effect factors and adjusts failure generating algorithm.
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A4,A5: After accepting the information about performing a mission, Main Controller first performs work preparation and initializes all the state parameter, then puts the work in operation and starts up stochastic failure generating mechanism.
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A6:If not in failure or accepting the information about halt in operation, then CA puts into the state of awaiting orders.
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A7: In consideration of maintenance in the course of reliability simulation, after failure, CA carries out maintenance on demand of SA. Here CA starts up a stochastic maintenance course, after the event, CA informs the message of being repaired to affiliated SA, and waits for the next order.
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A8, A9: If in operation, stochastic failure generation mechanism springs failure, Main Controller tranfers failure state and codes failure message to affiliated SA, waiting for the next deal notice.
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A10: It is done by Message Coder and Data Transmission Port.
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A11, A12: Data Accumulator gathers data automatically when internal state of Agent changing, outputs by chart and data file after data processing.
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A13,A14,A15：It cooperates with SA to finish system reliability state-checking. When CA accepts state-checking signals from subordinate SA, according to self state of CA, reliability relationship controller generates feedback signal, and transmits to downstream CA in reliability relationship with itself by R Downstream Port. Conclusions To the same large type and complex system as weapon equipment, to simulate mission environment-oriented dynamic reliability action for system, it sets up the system structure model of materiel system reliability simulation based on Adaptive Agent in the paper. Since the system reliability simulation based on Adaptive Agent is a simulation modeling technology from down to up, it is defined and introduced for a basis of modeling. Due to limited length, it emphasizes the devising thinking on Adaptive Agent from Conceptual Model, Structure Model and Function Model, not only to simulate component action in complex environment, but also to make reliability simulation of materiel system more close to the true, and provides powerful support for reliability analysis.
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