CN106447025B - Testability index distribution based on discrete particle cluster chooses integrated processes with test - Google Patents
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Abstract
The invention discloses a kind of, and the testability index distribution based on discrete particle cluster chooses integrated processes with test, related operating mode first in acquisition system, the related data of subsystem and test, then it uses discrete particle cluster algorithm to solve test and chooses result, whether corresponding test is selected in solution wherein in particle corresponding to each element representation current particle, in fitness function design, the gap of test cost and testability index and requirement is considered, global optimum's individual of discrete particle cluster algorithm iterative solution is chosen into result as test, testability index apportioning cost of each subsystem under each operating mode can be calculated by choosing result according to test.The present invention disposably realizes there are the distribution of the testability index of the system of multiple operating modes by the particle property and fitness function of rational design discrete particle cluster algorithm and tests selection.
Description
Technical field
The invention belongs to system level testing design optimizing fields, more specifically, are related to a kind of based on discrete
Integrated processes are chosen in testability index distribution and the test of population.
Background technology
What current Fault Diagnosis for Electronic System technical field mostly used is the modeling method of multi-signal model, system model
Testability index distribution be also based on multi-signal model, mainly the testability index of upper-level system or module is assigned to
The subsystem of lower layer, between the division of subsystem and there is no intersect with it is Chong Die, ensure that the isolation of intermodule physics, Er Qieyu
System hardware circuit combines even closer.Mainly there is experience point currently based on the method that the testability index of multi-signal model distributes
With method and linear interpolation distribution method.In terms of testing selection, the survey that testing cost is minimum in the case of meeting the requirements typically is selected
Examination, belongs to optimization problem.
But as technology develops, the scale of electronic system is increasing, and structure becomes increasingly complex, in this case,
There may be multiple operating modes, each operating modes can be related to different subsystems for one system.For this system, in order to
Ensure that the testability index under each operating mode can be met the requirements, needs that minimum test is respectively set to each operating mode
Property index request.In terms of testing selection, since the subsystem that each operating mode is related to is different, it can be used and test also not to the utmost
It is identical.And there may be intersections between the subsystem for by each operating mode being included, and are referred to using traditional testability
Mark distribution tests choosing method to realize that requirements above is relatively difficult, and implementation complexity is higher in other words, therefore is directed to
It is necessary that case above, which carries out testability index distribution method with test choosing method,.
Invention content
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of testability indexes based on discrete particle cluster
Distribution chooses integrated processes with test, disposable to realize that there are the distribution of the testability index of the system of multiple operating modes and tests
It chooses.
For achieving the above object, the present invention is based on the distribution of the testability index of discrete particle cluster combines with test selection
Method includes the following steps：
S1：Operating mode quantity N, the subsystem quantity H and test quantity M of system are obtained according to system information first；It obtains
Take subsystems ShNormalization crash rate αh, wherein h=1,2 ..., H,Count subsystems ShMistake
Effect pattern, by subsystem ShS-th of failure mode be denoted as fhs, wherein s=1,2 ..., | Sh|, | Sh| indicate subsystem ShMistake
Effect pattern quantity, by subsystem ShIn s-th of failure mode fhsFailure accounting be expressed as phs,It obtains each
Test tmNormalization test cost cm, wherein m=1,2 ..., M,Obtain each operating mode onWith subsystem
ShCorrespondence matrix K, element khn=1 indicates operating mode onNeed subsystem ShIt participates in, khn=0 indicates operating mode
onWithout subsystem ShIt participates in；Obtain each operating mode onWith test tmCorrespondence matrix T, element τmn=1 indicates to survey
Try tmIn operating mode onUnder can use, element τmn=0 indicates test tmIn operating mode onUnder it is unavailable；Obtain each test tm
To failure mode fhsVerification and measurement ratio matrix D, element dhsmIndicate test tmTo failure mode fhsVerification and measurement ratio；
S2：Test is solved using discrete particle cluster algorithm to choose as a result, wherein particle xi=(xi1,xi2,…,xiM) in
Element ximIndicate test t whether is selected in the solution corresponding to current particlem, work as xim=1 indicates test tmIt is selected, xim=0 table
Show test tmIt is not selected；Fitness function Fit employed in iterative processiExpression formula be：
Wherein, puinExpression formula be：
Fn_minIndicate operating mode onMinimum testability index requirement, FinIt indicates according to particle xiSelected measuring and calculation
Obtained operating mode onTestability index；
Obtained global optimum's individual will be finally solved after iterative solutionKnot is chosen as test
Fruit；
S3：It is chosen according to test as a result, calculating each subsystem ShIn operating mode onUnder testability index apportioning cost
γhn, calculation formula is：
The present invention is based on the distribution of the testability index of discrete particle cluster to choose integrated processes with test, first in acquisition system
In relation to operating mode, subsystem and the related data of test, then use discrete particle cluster algorithm solve test choose as a result, its
Whether corresponding test is selected in solution in middle particle corresponding to each element representation current particle, in fitness function design,
The gap for having considered test cost and testability index and requirement, the global optimum that discrete particle cluster algorithm is iteratively solved
Individual is chosen as test as a result, testability of each subsystem under each operating mode can be calculated by choosing result according to test
Distribution Indexes value.The present invention passes through the particle property and fitness function of rational design discrete particle cluster algorithm, disposable realization
There are the distribution of the testability index of the system of multiple operating modes to choose with test.
Description of the drawings
Fig. 1 is that the present invention is based on the specific implementations of the distribution of the testability index of discrete particle cluster and test selection integrated processes
Mode flow chart.
Specific implementation mode
The specific implementation mode of the present invention is described below in conjunction with the accompanying drawings, preferably so as to those skilled in the art
Understand the present invention.Requiring particular attention is that in the following description, when known function and the detailed description of design perhaps
When can desalinate the main contents of the present invention, these descriptions will be ignored herein.
In the present invention, discrete particle cluster algorithm is applied to system level testing optimization design, it is disposable to realize test
Property index optimization distribution and test it is preferred.Technical solution in order to better illustrate the present invention is first based on the present invention
Discrete particle cluster algorithm is briefly described.
Particle cluster algorithm (Particle Swarm Optimization, PSO) is Kennedy and Eberhart by flock of birds
A kind of optimization algorithm based on population that the inspiration of foraging behavior is proposed in nineteen ninety-five.Based on individual (such as particle) row in group
For and mathematical abstractions developed PSO algorithms, algorithm uses Speed-position model, i.e. PSO algorithms to be initialized as within the allowable range
A group random particles (potential solution), each particle determine their heading and distance there are one speed, change each time
In generation oneself is updated by tracking two extreme values：The individual extreme value Pbest that particle itself is found so faridWith entire kind
The global extremum Gbest that group is found so farid.The adaptive values that are determined by optimised object of quality of all particles weighs
Amount.
It is located at the search space of M dimensions, a group is formed by Y particle, i-th of particle in PSO optimization algorithms
Position and speed be represented by:xi=(xi1,xi2,...,xiM) and vi=(vi1,vi2,...,viM), wherein i=1,2 ..., Y；
Correspondingly, the optimal location that i-th of particle searches so far is recorded asEntirely
The optimal location that population searches so far isUtilize these information, PSO algorithms
The speed of i-th particle and position are updated using formula (1) (2).
In formula,For particle i the t times iteration speed；M=1,2 ..., M；Vmin_mWith
Vmax_mIt is the minimum and maximum limit to particle rapidity, particle rapidity is too high, is easy far from target area, the too low appearance of particle rapidity
It easily is absorbed in local optimum, optimal particle cannot be selected；q1、q2For aceleration pulse；r1、r2It is the random number between 0 to 1；For grain
Sub- i is in the position of the t times iteration.
Since PSO algorithms have the characteristics that fast convergence and global optimizing ability are strong, it is successfully applied to very by people
It is multi-field.However many optimization problems are described in discrete space, such as traveling salesman problem, job shop scheduling problem, route planning
Problem etc., therefore PSO (the discrete binary that Kennedy and Eberhart proposed discrete binary version in 1997
Particle swarm optimization, DPSO) algorithm so that particle cluster algorithm can be more broadly used for discrete space and ask
Topic, they often one-dimensional will be limited to 1 in the model of proposition or be 0, and speed does not make this limitation.Position is indicated with speed
The possibility that state changes, with speed come when updating position, if value is larger, the position of particle is more likely 1, and value is small by one
Point may be then 0.Since speed is a probability value, then it should be limited between [0,1], and Sigmoid functions (3) are just
Has this feature.
There is no change or formula (1) for the more new formula of particle rapidity defined in DPSO, and location formula (2) becomes
Formula (4)：
Wherein, the random number that rand is 0~1.
The present invention utilize discrete particle cluster algorithm the characteristics of, it is proposed that based on discrete particle cluster testability index distribution with
Integrated processes are chosen in test.Fig. 1 is that the present invention is based on the distribution of the testability index of discrete particle cluster chooses integrated processes with test
Specific implementation mode flow chart.It is chosen with test as shown in Figure 1, the present invention is based on the distribution of the testability index of discrete particle cluster
The specific steps of integrated processes include：
S101：Acquisition system related data：
Operating mode quantity N, the subsystem quantity H and test quantity M of system are obtained according to system information first；It obtains each
A subsystem ShNormalization crash rate αh, wherein h=1,2 ..., H,Count subsystems ShFailure mould
Formula, by subsystem ShS-th of failure mode be denoted as fhs, wherein s=1,2 ..., | Sh|, | Sh| indicate subsystem ShFailure mould
Formula quantity, by subsystem ShIn s-th of failure mode fhsFailure accounting be expressed as phs,Obtain each test
tmNormalization test cost cm, wherein m=1,2 ..., M,Obtain each operating mode onWith subsystem Sh's
Correspondence matrix K, element khn=1 indicates operating mode onNeed subsystem ShIt participates in, khn=0 indicates operating mode onNothing
Need subsystem ShIt participates in；Obtain each operating mode onWith test tmCorrespondence matrix T, element τmn=1 indicates test tm
In operating mode onUnder can use, element τmn=0 indicates test tmIn operating mode onUnder it is unavailable；Obtain each test tmTo losing
Effect pattern fhsVerification and measurement ratio matrix D, element dhsmIndicate test tmTo failure mode fhsVerification and measurement ratio.
It is assumed that certain system has 3 operating modes, 5 subsystems, 5 available tests.The example of so matrix K is：
In the case of the 3rd of matrix K row, the 1st, 3, the values of 4 rows be 1, represent operating mode o3Need S1、S3、S4Three subsystems
System normal work could be completed, and sub-systems are unrelated with this operating mode.
It is assumed that each subsystem normalization crash rate is：
3 operating modes and the correspondence matrix T of 5 available tests are：
In the case of the 1st row, operating mode o1In can be t with test1And t4, therefore τ11=τ41=1.Other tests are in this work
It is unavailable under operation mode, then τ21=τ31=τ51=0.
Each subsystem is assumed in the present embodiment, and there are two failure modes.Table 1 is the form of verification and measurement ratio matrix D.
| dhsm | t1 | t2 | t3 | t4 | t5 |
| f11 | 0 | 0.9 | 0.3 | 0.9 | 0.3 |
| f12 | 0 | 0.9 | 0.3 | 0.9 | 0.9 |
| f21 | 0.7 | 0 | 0 | 0 | 0 |
| f22 | 0.7 | 0 | 0 | 0 | 0 |
| f31 | 0 | 0 | 0.7 | 0.7 | 0.7 |
| f32 | 0 | 0 | 0.9 | 0.7 | 0.3 |
| f41 | 0 | 0 | 0.7 | 0.7 | 0.7 |
| f42 | 0 | 0 | 0.9 | 0.9 | 0.7 |
| f51 | 0 | 0.3 | 0 | 0 | 0.7 |
| f52 | 0 | 0.9 | 0 | 0 | 0.3 |
Table 1
Such as d in table 1313=0.7, indicate test t3It can be by subsystem S3No. 1 failure mode f31The probability that detected
It is 0.7.
By subsystem and failure mode relationship, the information such as test cost and failure mode crash rate, which summarize, is added above-mentioned matrix
Obtain system data table related.Table 2 is system data table related in the present embodiment.
Table 2
As shown in table 2, data in the bracket of lower section are tested and indicate that its test cost, all expenses are normalized.
Data indicate its normalization failure probability in subsystem in bracket behind failure mode.Data in the bracket at subsystem rear
Indicate its normalization crash rate in systems.
S102：Test based on discrete particle cluster is chosen：
Testability index distribution and test selection are carried out to combine using discrete particle cluster algorithm, it is necessary first to determine grain
The fitness function of the attribute of son.In the present invention, the target of testability design is the case where meeting testability index requirement
The lower expense for minimizing test (or testability design) is chosen due to combine the distribution of progress testability index with test,
It should consider testability index when building fitness function, also to consider test cost.
Testability index is analyzed first.In operating mode onUnder, the minimum testability index requirement of note system
For Fn_min, therefore have：
0≤γhn≤1 (9)
Wherein, FnIndicate operating mode onTestability index of the lower system under selected test, h indicate subsystem serial number, n
Indicate operating mode serial number, αhIndicate subsystem ShNormalization crash rate, khnIndicate subsystem ShWhether operating mode is belonged to
on, γhnIndicate operating mode onLower subsystem ShThe testability index being calculated after selected test.There is N number of work due to total
Operation mode, therefore (8), (9) formula constraints have N number of.
Binding test is chosen, then optimization aim is in the case where meeting N number of constraints, minimizes integrated testability and opens
Pin, is indicated with following formula：
Wherein, xm=1 indicates test tmIt is selected, xm=0 indicates test tmIt is not selected.So subsystem ShIt is surveyed selected
Under examination, in operating mode onUnder the testability index γ that is calculatedhnExpression formula be：
In testability index, it is mainly Percent Isolated (FIR), fault detect rate to need the index being allocated
(FDR), other can test index generally do not have to distribution.Therefore with operating mode o in step S101 examples2For, provide its event
Hinder the computational methods of verification and measurement ratio.The operating mode o it can be seen from matrix T2Under can be t with test2And t5, therefore τ22=τ52=
1.Unavailable under this operating mode, the τ of other tests12=τ32=τ42=0.From matrix K as can be seen that there are two this pattern contains
Subsystem S1And S5.Therefore k12=k52=1, k22=k32=k42=1.The calculation formula of so fault detect rate is：
Assuming that test t2And t5It is all selected, i.e. x2=x5=1.Subsystem S can be calculated1Failure mode f11、f12In work
Operation mode o2Under fault detect rate be respectively：
It is not difficult to calculate,γ can similarly be calculated52=
0.818, so having：
The fault detect rate computational methods of other operating modes are similar with the above process.It is that all test is all selected above
The case where, normalization test cost at this time is 1.If the fault detect rate minimum requirements of No. 2 operating modes is 90%, it is clear that
97% verification and measurement ratio can be reduced suitably, to reduce testing cost.And the present invention is excellent for solving this by discrete particle cluster algorithm
Change problem so that testing cost is minimum while meeting each operating mode fault detect rate and requiring, while realizing from work
The testability index of pattern testability index to each subsystem distributes.
According to the above analysis it is found that in the discrete particle cluster of the present invention, each particle xi=(xi1,xi2,…,xiM) in
Element ximIndicate test t whether is selected in the solution corresponding to current particlem, it is clear that ximThere are two possible values, work as xim=1 table
Show test tmIt is selected, xim=0 indicates test tmIt is not selected.Whether the particle optimal in population, with fitness function come
It weighs.Fitness function mainly includes the expense summation of selected test in the present inventionWith testability index and requirement
Gap pun, punExpression formula be：
Obviously,Or punBigger, then individual is poorer.Therefore, particle x defined in the present inventioniFitness function table
It is up to formula：
Wherein, puinExpression formula be：
Fn_minIndicate operating mode onMinimum testability index requirement, FinIt indicates according to particle xiSelected measuring and calculation
Obtained operating mode onTestability index, calculation formula is：
Indicate operating mode onLower subsystem ShIn particle xiThe testability index being calculated under selected test, root
According to known to formula (11)：
Obviously, FitiBigger, corresponding particle individual is better：The cost of corresponding test is lower and can meet testability
Index request.
Therefore, solution is iterated using discrete particle cluster algorithm according to preset maximum iteration, will finally obtained
Global optimum individualResult is chosen as test.
S103：Calculate testability index apportioning cost：
It is chosen according to test as a result, calculating each subsystem ShIn operating mode onUnder testability index apportioning cost γhn, root
It is according to its calculation formula known to formula (11)：
Embodiment
In order to illustrate the technique effect of the present invention, experimental verification is carried out using a specific system example.Table 3 is this implementation
Objective system employed in example.
Table 3
As shown in table 3, this system is made of 7 subsystems, a total of 4 operating modes, and each operating mode needs reach
Minimum fault detect rate as shown in 1 second row of table, i.e., the minimum fault detect rate of each operating mode is respectively FDR1_min=
0.85、FDR2_min=0.7, FDR3_min=0.85, FDR4_min=0.7.Secondary series provides the normalization crash rate of each subsystem.
Table 4 is the available test correspondence under each operating mode.
| o1 | o2 | o3 | o4 | |
| t1 | 1 | 0 | 0 | 0 |
| t2 | 1 | 0 | 0 | 0 |
| t3 | 0 | 1 | 0 | 0 |
| t4 | 0 | 1 | 0 | 0 |
| t5 | 0 | 0 | 1 | 0 |
| t6 | 0 | 0 | 1 | 0 |
| t7 | 0 | 0 | 0 | 1 |
| t8 | 0 | 0 | 0 | 1 |
| t9 | 0 | 0 | 1 | 0 |
| t10 | 0 | 0 | 1 | 1 |
Table 4
As shown in table 4, whole system can use test 10 total in the present embodiment, and 1 indicates corresponding operating mode in table
The lower test is available, and 0 indicates unavailable.
Table 5 is the corresponding test matrix of the present embodiment system.
Table 5
5 (Continued) of table
As shown in table 5, each subsystem is there are three failure mode in the present embodiment, and third is classified as each failure mode in table 3
Normalization crash rate in its subsystem, the expense of each test of the second behavior, other elements are each test to each failure mould
The verification and measurement ratio of formula.
The optimal test set of integrated processes using the present invention, acquisition is [1,1,1,1,1,0,0,0,0,1].I.e. 1,2,3,
4,5 and No. 10 tests are selected.According to the test cost of each test, it is not difficult to calculate total test cost to be 0.5751.According to
It is FDR that upper selected test, which calculates the fault detect rate under each operating mode,1=0.8473, FDR2=0.8684, FDR3=
0.8905, FDR4=0.7516, it meets the requirements.Table 6 is the fault detect rate that each subsystem is assigned in each mode.
| o1 | o2 | o3 | o4 | |
| S1 | 0.8821 | 0 | 0 | 0 |
| S2 | 0 | 0 | 0.9597 | 0 |
| S3 | 0.9430 | 0 | 0 | 0 |
| S4 | 0 | 0 | 0.9647 | 0.6764 |
| S5 | 0.7479 | 0.8387 | 0.8857 | 0.6730 |
| S6 | 0 | 0 | 0.8286 | 0 |
| S7 | 0 | 0.8950 | 0 | 0.8678 |
Table 6
According to the above specific example it is found that being selected with test using the present invention is based on the distribution of the testability index of discrete particle cluster
Integrated processes are taken, can disposably realize that there are the distribution of the testability index of the system of multiple operating modes and be chosen with test.
Although the illustrative specific implementation mode of the present invention is described above, in order to the technology of the art
Personnel understand the present invention, it should be apparent that the present invention is not limited to the range of specific implementation mode, to the common skill of the art
For art personnel, if various change the attached claims limit and determine the spirit and scope of the present invention in, these
Variation is it will be apparent that all utilize the innovation and creation of present inventive concept in the row of protection.
Claims (1)
1. it is a kind of based on discrete particle cluster testability index distribution with test choose integrated processes, which is characterized in that including with
Lower step：
S1：Operating mode quantity N, the subsystem quantity H and test quantity M of system are obtained according to system information first；It obtains each
A subsystem ShNormalization crash rate αh, wherein h=1,2 ..., H,Count subsystems ShFailure mould
Formula, by subsystem ShS-th of failure mode be denoted as fhs, wherein s=1,2 ..., | Sh|, | Sh| indicate subsystem ShFailure mould
Formula quantity, by subsystem ShIn s-th of failure mode fhsFailure accounting be expressed as phs,Obtain each test
tmNormalization test cost cm, wherein m=1,2 ..., M,Obtain each operating mode onWith subsystem Sh's
Correspondence matrix K, element khn=1 indicates operating mode onNeed subsystem ShIt participates in, khn=0 indicates operating mode onNothing
Need subsystem ShIt participates in, n=1,2 ..., N；Obtain each operating mode onWith test tmCorrespondence matrix T, element τmn
=1 indicates test tmIn operating mode onUnder can use, element τmn=0 indicates test tmIn operating mode onUnder it is unavailable；It obtains each
A test tmTo failure mode fhsVerification and measurement ratio matrix D, element dhsmIndicate test tmTo failure mode fhsVerification and measurement ratio；
S2：Test is solved using discrete particle cluster algorithm to choose as a result, wherein particle xi=(xi1,xi2,…,xiM) in element
ximIndicate test t whether is selected in the solution corresponding to current particlem, work as xim=1 indicates test tmIt is selected, xim=0 indicates to survey
Try tmIt is not selected；Fitness function Fit employed in iterative processiExpression formula be：
Wherein, puinExpression formula be：
Fn_minIndicate operating mode onMinimum testability index requirement, FinIt indicates according to particle xiSelected measuring and calculation obtains
Operating mode onTestability index；
Obtained global optimum's individual will be finally solved after iterative solutionResult is chosen as test；
S3：Result, which is chosen, according to test calculates each subsystem ShIn operating mode onUnder testability index apportioning cost γhn, calculate
Formula is：
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