Study On Testability For Electronic System Based On Particle Swarm Optimization Algorithm

With the increased integration of electronic products,electronic system becomesmore and more complex and needs a higher demand of testability objectively.Forelectronic system testability,test resource must be collocated and distributed in thewhole system;on the other side,the accuracy and efficiency of testability study methodshave to be advanced to reduce test cost because of the complexity of electronic system.Therefore,any single testability study method such as graph theory-based method,information entropy-based algorithm and symbol analysis-based method for specificcircuit can not satisfy testability demand and arrange test resource comprehensively andreasonably.In recent years,a particle swarm optimization algorithm is applied widelyowning to its characteristics of fast convergence and easy to realize in engineering.According to characteristics of electronic systems,this dissertation presents a studymethod of electronic system testability based on particle swarm optimization algorithm.The main work is as follows:1.Study on testability model of electronic system.On the basis of comparingexisting testability models,the superiority of using multi-signal model for electronicsystem testability and detailed steps of constructing electronic system testability modelvia radar transmitter system is presented.It lays the foundation for electronic systemtestability study.2.Study on test points selection of electronic system based on multidimensionalfitness function discrete particle swarm optimization(MDFDPSO) algorithm.Testpoints selection in electronic system is converted to a multi-objective optimizationproblem,a multidimensional fitness function discrete particle swarm optimizationalgorithm is proposed to select test points and solve multi-objective optimizationproblem.This algorithm defined a multidimensional fitness function,the number ofdimensions in its fitness function is the same as the number of optimal goals,eachdimension of the fitness function is a goal in test points selection problem.The multiplegoals can be optimized at one time via the particle searching in MDFDPSO algorithm.An elitist set is added in MDFDPSO to guarantee global optimization capability. Examples in the dissertation validated that MDFDPSO improves efficiency of testpoints selection and has good global optimization characteristic when it is comparedwith other methods.It provides a new way to improve particle swarm optimizationalgorithm and solve multi-objective optimization problem.3.Study on optimal test strategy problem based on particle swarm optimizationand improved AO~*.For the case that current methods have disadvantage in localoptimization and"computation explosion"when the system is too large,a combinationmethod of particle swarm optimization and improved AO~* is proposed in thisdissertation to design the optimal test strategy in electronic system on the foundation ofthe best test sets selected by MDFDPSO.This method uses particle swarm optimizationalgorithm to select test in each node to expand by AO~* in the process of designing thebest test strategy.It decreases the number of test and trace by AO~*,reduces thecomplexity of computation and improves computational efficiency.Because the root ofAO~* is the test set selected by MDFDPSO,this method can satisfy the global optimaldemand in best test strategy design for electronic system.4.Study on identifying masking false failure in electronic system based on particleswarm optimization.Identifying masking false failure is a difficult problem in electronicsystem testability analysis.This dissertation converts it to minimal hitting set matter andpresents a method based on particle swarm optimization algorithm to solve minimalhitting set problem.It can attain all masking false failure in system through hidden faultand the minimal hitting set of masking false failure and avoids"CombinatorialExplosion"for large-scale system compared with existing methods.It is especiallysuitable for identifying masking false failure in large-scale complex electronic systemand provides a new method of solving minimal hitting set problem.5.Study on finding ambiguity groups in analog circuit via variation particle swarmoptimization algorithm.Finding ambiguity groups in analog circuit is difficult,avariation particle swarm optimization algorithm is proposed.Based on symbol analysismethod,it constructs the testability matrix of analog circuit,decomposes the vector intestability matrix into base vector and non-base vector,and can quickly find allambiguity groups in analog circuit via the variation of velocity in particle swarmoptimization algorithm.This method drastically eliminates the errors caused by originaltriangular factorization method and boosts the computation precision. 6.Study on improvement and multiple fault diagnosis of electronic system basedon testability analysis.Testability guarantees the ability of electronic system faultdiagnosis;this dissertation evaluates the fault diagnosis ability via testability analysisand improves the system whose testability is poor.If ther are hidden faults and maskingfalse failures in electronic system,"multiple fault concurrent-single failurephenomenon"can easily appear.The method proposed in this dissertation canefficiently isolates the faulty components in this phenomenon through testabilityanalysis and makes up the shortage in original multiple fault diagnosis method.