CN110619148A - Equipment ADC (analog to digital converter) efficiency evaluation method based on interval gray number - Google Patents
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Abstract
The invention provides an equipment ADC (analog to digital converter) efficiency evaluation method based on interval gray number, which comprises the following steps: and (3) PBS decomposition step: performing PBS decomposition on the equipment; a characterization step: representing the state transition of each subsystem of the equipment; ADC efficiency evaluation modeling step: constructing an ADC efficiency evaluation model; constructing a probability density matrix: constructing a gray state transition probability density matrix Q; and solving the availability: constructing a state transition balance equation set, and solving equipment availability A under a steady-state condition; and calculating the credibility: calculating equipment reliability D according to a Kolmogorov forward equation; acquiring a capacity matrix: acquiring an equipment capacity matrix C; calculating the system efficiency of the equipment: equipment system performance information is obtained. The invention provides an effective method for the efficiency evaluation of the complex equipment under the conditions of uncertain failure and maintenance parameters, and can be widely applied to the efficiency evaluation of the complex equipment.
Description
Technical Field
The invention relates to the field of performance evaluation, in particular to an equipment ADC performance evaluation method based on interval gray numbers, and particularly relates to a complex equipment ADC performance evaluation method based on interval gray numbers.
Background
The performance evaluation methods of the complex information system are numerous, and an ADC (ADC EIAC) model is a currently accepted effective performance evaluation model, can be used for analyzing variables, has good formula transparency, and is easy to understand and calculate. When the ADC (wseiac) model is applied to complex equipment, uncertainty of ADC performance evaluation work occurs. The uncertainty of the performance evaluation work of the ADC of the complex equipment is represented by the aspects of multi-source heterogeneity of equipment test data, difference of equipment maintenance service levels, imbalance of equipment subsystem development levels and the like. The classical ADC evaluation method assumes that the failure rate, the repair rate and the capability matrix of the equipment are all determined parameters, equipment availability and reliability parameters are solved in a determined Mahalanobis process, and then equipment efficiency value calculation is carried out.
Patent document 106815426a discloses a missile autonomous formation comprehensive combat effectiveness evaluation method, which includes the following steps: establishing a missile autonomous formation comprehensive combat effectiveness analysis model; step two: determining the level of cooperative guidance and control capability; the method comprises the following specific steps: step 1, establishing a hierarchical structure, and decomposing the autonomous formation cooperative guidance control capability of the missiles layer by layer; step 2, calculating the combination weight of the bottom layer elements of the hierarchical structure: according to a simple table method, filling a square root by experts to obtain a table, respectively calculating corresponding judgment matrixes corresponding to weight tables of each layer of capability, and solving a feature vector; step 3, providing evaluation values of various evaluation contents according to the research of various current subsystems; the value range e of the index value belongs to [0,10 ]; and further obtaining the evaluation value of the guided missile autonomous formation cooperative guidance and control capability. The patent does not use an interval gray number-based complex equipment ADC efficiency evaluation technology, cannot comprehensively utilize reliability data, maintainability data and performance index data of a system to evaluate corresponding efficiency, and can cause uncertainty of efficiency evaluation work if the method is suitable for complex equipment.
Disclosure of Invention
In view of the defects in the prior art, the present invention provides a method for evaluating the performance of an ADC based on interval gray number.
The equipment ADC efficiency evaluation method based on interval gray number provided by the invention comprises the following steps: and (3) PBS decomposition step: performing PBS decomposition on the equipment, and determining the reliability coupling relation of each operation subsystem of the equipment; a characterization step: characterizing the state transition of each subsystem of the equipment based on a Markov process; ADC efficiency evaluation modeling step: constructing an ADC efficiency evaluation model; constructing a probability density matrix: according to the reliability and maintainability data of each subsystem, state transition parameters are represented by interval grey numbers, and a grey state transition probability density matrix Q is constructed; and solving the availability: constructing a state transition balance equation set, and solving equipment availability A under a steady-state condition; and calculating the credibility: calculating equipment reliability D according to a Kolmogorov forward equation; acquiring a capacity matrix: acquiring an equipment capacity matrix C according to the capacity index; calculating the system efficiency of the equipment: and acquiring the efficiency information of the equipment system according to the availability A, the credibility D and the equipment capacity matrix C.
Preferably, the ADC performance evaluation model is: e ═ axxdxc; wherein A is the availability vector of the system; the D matrix is a credibility matrix; the C matrix is a capability matrix of the system.
Preferably, the step of characterizing comprises: and (3) state transition modeling step: and aiming at the main subsystems possibly involved in the working state, on the basis of considering the working relation coupling, carrying out state transition modeling on the repairable system according to the failure and repair process.
Preferably, the markov process is a stochastic process satisfying markov property.
Preferably, the matrix Q is represented by the following method:
wherein the content of the first and second substances,is a common interval grey number notation;is an n multiplied by n matrix characterized by the gray number of the general interval, and represents that the system has n meaningful transition states; whereinThe transition probability density of the equipment subsystem staying in the 1 state is calculated by the following rule: satisfy the requirement ofEach row of the matrix is summed to 0; can in turn calculateThe diagonal element value of (a);representing the transition probability density of the system transitioning from state 1 to state 2,the efficiency of the repair of the system is shown,all elements are set according to the rule; whereinIs determined by the actual system reliability diagram, and the specific parameter value is determined by the actual equipment subsystem reliability parameter.
Preferably, the system of equilibrium equations satisfies the formula:
wherein pi is a system steady-state probability column vector, n is the number of states,is a common interval grey scale notation.
Preferably, the method further comprises the following steps: and acquiring a mapping map: and acquiring the efficiency mapping chart information according to the equipment system efficiency information.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention runs through a key technology of equipment development, test and evaluation, and comprehensively utilizes the reliability data, maintainability data and performance index data of the equipment to evaluate the efficiency of the corresponding equipment;
2. according to the method, the uncertainty of the performance evaluation parameter acquisition possibly caused by the multisource heterogeneity of equipment test data, the difference of equipment maintenance service levels and the imbalance of equipment subsystem development levels is comprehensively considered, so that the equipment failure rate and the repair rate are characterized by the ash number of the generally distributed intervals, a grey Markov process is constructed, and further the solution of relevant parameters is carried out, and the method is suitable for both an analytic method and a simulation method;
3. the invention can deal with the uncertainty of the performance evaluation work of the equipment ADC and can adapt to the requirement of the performance evaluation of complex equipment.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of a modeling flow in an embodiment of the invention;
fig. 3 is a schematic diagram of a specific model of unmanned aerial vehicle failure rate/recovery rate versus performance mapping in an embodiment of the invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in fig. 1 and fig. 2, the method for evaluating the performance of an equipment ADC based on interval gray scale according to the present invention includes: and (3) PBS decomposition step: performing PBS decomposition on the equipment, and determining the reliability coupling relation of each operation subsystem of the equipment; a characterization step: characterizing the state transition of each subsystem of the equipment based on a Markov process; ADC efficiency evaluation modeling step: constructing an ADC efficiency evaluation model; constructing a probability density matrix: according to the reliability and maintainability data of each subsystem, state transition parameters are represented by interval grey numbers, and a grey state transition probability density matrix Q is constructed; and solving the availability: constructing a state transition balance equation set, and solving equipment availability A under a steady-state condition; and calculating the credibility: calculating equipment reliability D according to a Kolmogorov forward equation; acquiring a capacity matrix: acquiring an equipment capacity matrix C according to the capacity index; calculating the system efficiency of the equipment: and acquiring the efficiency information of the equipment system according to the availability A, the credibility D and the equipment capacity matrix C.
The invention provides an interval gray number-based complex equipment ADC (analog to digital converter) efficiency evaluation method, which aims to adapt to multi-source heterogeneity of equipment test data, difference of equipment maintenance service levels and equipment subsystem state transition matrix modeling of an equipment subsystem development level imbalance background on the one hand, expand a parameter simulation range of a model and solve part of special distribution by using a simulation method on the other hand.
The ADC performance evaluation model is: e ═ axxdxc; wherein A is the availability vector of the system; the D matrix is a credibility matrix; the C matrix is a capability matrix of the system.
The ADC model is: e ═ axxdxc;
where a is the Availability (Availability) vector of the system, representing the probability that the system is in a different state at the moment the system starts to execute a task. The D matrix is a credibility (dependency) matrix and represents the probability that the system is in a certain state at the beginning and is transferred to another state in the using process, the C matrix is a Capability (Capability) matrix of the system, and different Capability index systems are constructed for different complex information systems through Capability index calculation. Because the index system has hierarchy and large quantity, the ability value is calculated by adopting an Analytic Hierarchy Process (AHP). After the composition and initial state of the complex information system are determined and the working state possibly appearing in the task is completed, the availability matrix and the credibility matrix are changed into quantitative matrices, and the capacity matrix influencing the efficiency value of the complex system is obtained through analysis.
The characterizing step includes: and (3) state transition modeling step: and aiming at the main subsystems possibly involved in the working state, on the basis of considering the working relation coupling, carrying out state transition modeling on the repairable system according to the failure and repair process.
The Markov process is a process in which a stochastic process satisfies Markov.
The matrix Q is represented as follows:
wherein the content of the first and second substances,is a common interval grey number notation;is an n multiplied by n matrix characterized by the gray number of the general interval, and represents that the system has n meaningful transition states; whereinThe transition probability density of the equipment subsystem staying in the 1 state is calculated by the following rule: satisfy the requirement ofEach row of the matrix is summed to 0; can in turn calculateThe diagonal element value of (a);representing the transition probability density of the system transitioning from state 1 to state 2,the efficiency of the repair of the system is shown,all elements are set according to the rule; whereinIs determined by the actual system reliability diagram, and the specific parameter value is determined by the actual equipment subsystem reliability parameter.
The system of equilibrium equations satisfies the formula:
wherein pi is a system steady-state probability column vector, n is the number of states,is a common interval grey scale notation.
Further comprising: and acquiring a mapping map: and acquiring the efficiency mapping chart information according to the equipment system efficiency information.
Specifically, in one embodiment, the performance evaluation method for a particular model of drone is as follows:
in step 1, a model of unmanned plane PBS is decomposed into: unmanned aerial vehicle aircraft (2 sets), ground control system, mission planning and control station, data link, transmission and recovery subsystem.
In step 2, the unmanned aerial vehicle (payload), the ground control system, the mission planning and control station, the data link, the launching and recovery subsystems are in a series connection relationship, namely, one subsystem completely fails, the whole system is in a failure state, the mission cannot continue, and the unmanned aerial vehicle is in a parallel connection relationship.
In step 3, with x1,x2,x3,x4,x5Respectively representing a ground control system, a task planning and control station, a data link, a transmitting and recovering subsystem and an aircraft subsystem, and assuming that each subsystem only has one repairer, no repairer exists in the task preparation and execution stagesThe human scout system mainly has the following states: (1) all subsystems are in a normal state; (2)1 aircraft subsystem fails, and the rest subsystems are normal; (3)2 aircraft subsystems have faults, and the rest subsystems are normal; (4) x is the number of1Failure, the rest is normal; (5) x is the number of2Failure, the rest is normal; (6) x is the number of3Failure, the rest is normal; (7) x is the number of4Failure, the rest is normal; (8)1 x5And x1Failure, the rest is normal; (9)1 x5And x2Failure, the rest is normal; (10)1 x5And x3Failure, the rest is normal; (11)1 x5And x4Failure, the rest is normal.
In step 4, step 5,
the probabilities of the system being in 11 possible states at steady state can be obtained by the Q-process balance equation system, and the sum of the probabilities of the operating states {1,2} is the availability of the system. The equilibrium equation set of the Q-process of the unmanned reconnaissance aircraft system is as follows:
wherein, piiThe probability of the system in the state i in the steady state is shown, the availability of the unmanned reconnaissance plane is as follows:
in step 6, the Kolmogorov progression equation is as follows:
to reduce the computational effort, only the probability that the system is in the working state at the moment of the task is solved here. The Q matrix is divided into four blocks:whereinThe probability rate matrix representing the transitions between 2 non-absorbing states (operating states.) in combination with the kolmogorov forward equation:wherein p isw(t) is the probability of completing a specified task under the system working state
Assuming that all subsystems are in normal working state when task starts, i.e. p1(0)＝1,p2(0) And (4) performing pull transformation and solving on the obtained 0, wherein the obtained system is always in a working state {1,2} probability p in the process of executing the task1(t),p2(t)
As shown in fig. 3, the failure rate/repair rate versus performance map of a specific model of drone can be clearly shown in the failure rate/repair rate versus performance map of a certain model of drone.
The invention runs through a key technology of equipment development, test and evaluation, and comprehensively utilizes the reliability data, maintainability data and performance index data of the equipment to evaluate the efficiency of the corresponding equipment; according to the method, the uncertainty of the performance evaluation parameter acquisition possibly caused by the multisource heterogeneity of equipment test data, the difference of equipment maintenance service levels and the imbalance of equipment subsystem development levels is comprehensively considered, so that the equipment failure rate and the repair rate are characterized by the ash number of the generally distributed intervals, a grey Markov process is constructed, and further the solution of relevant parameters is carried out, and the method is suitable for both an analytic method and a simulation method; the invention can deal with the uncertainty of the performance evaluation work of the equipment ADC and can adapt to the requirement of the performance evaluation of the equipment.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (7)
1. An equipment ADC performance evaluation method based on interval gray number is characterized by comprising the following steps:
and (3) PBS decomposition step: performing PBS decomposition on the equipment, and determining the reliability coupling relation of each operation subsystem of the equipment;
a characterization step: characterizing the state transition of each subsystem of the equipment based on a Markov process;
ADC efficiency evaluation modeling step: constructing an ADC efficiency evaluation model;
constructing a probability density matrix: according to the reliability and maintainability data of each subsystem, state transition parameters are represented by interval grey numbers, and a grey state transition probability density matrix Q is constructed;
and solving the availability: constructing a state transition balance equation set, and solving equipment availability A under a steady-state condition;
and calculating the credibility: calculating equipment reliability D according to a Kolmogorov forward equation;
acquiring a capacity matrix: acquiring an equipment capacity matrix C according to the capacity index;
calculating the system efficiency of the equipment: and acquiring the efficiency information of the equipment system according to the availability A, the credibility D and the equipment capacity matrix C.
2. The interval gray number-based equipment ADC performance evaluation method of claim 1, wherein the ADC performance evaluation model is:
E＝A×D×C；
wherein A is the availability vector of the system; the D matrix is a credibility matrix; the C matrix is a capability matrix of the system.
3. The interval gray number-based equipment ADC performance evaluation method of claim 1, wherein the characterizing step comprises:
and (3) state transition modeling step: and aiming at subsystems related to the working state, on the basis of considering the working relation coupling, carrying out state transition modeling capable of repairing the system according to the failure and repair process.
4. The interval gray number-based equipment ADC performance evaluation method of claim 1, wherein the markov process is a process where a stochastic process satisfies the markov property.
5. The interval gray number-based equipment ADC performance evaluation method of claim 1, wherein the matrix Q is represented as follows:
wherein the content of the first and second substances,is a common interval grey number notation;is an n multiplied by n matrix characterized by the gray number of the general interval, and represents that the system has n meaningful transition states;
wherein the content of the first and second substances,the transition probability density of the equipment subsystem staying in the 1 state is calculated by the following rule: satisfy the requirement ofThe summation of each row of the matrix is 0, which in turn can be calculatedThe diagonal element value of (a);
representing the transition probability density of the system transitioning from state 1 to state 2,representing the repair efficiency of the system;
all elements are set according to the rule; whereinIs determined by the actual system reliability diagram, and the specific parameter value is determined by the actual equipment subsystem reliability parameter.
6. The interval gray number based equipment ADC performance evaluation method of claim 1, wherein the system of equilibrium equations satisfies the formula:
wherein pi is a system steady-state probability column vector, n is the number of states,in order to record the grey number of the general interval,
is a grey state transition probability density matrix.
7. The interval gray number-based equipment ADC performance evaluation method of claim 1, further comprising:
and acquiring a mapping map: and acquiring the efficiency mapping chart information according to the equipment system efficiency information.
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Application publication date: 20191227 |