Testability virtual test is a new test method for testability verification,which has the advantages such as low cost,few restrictions and large sample of test data. It can be used to make up the deficiency of testability physical test. In order to take the advantage of testability virtual test data effectively and to improve the accuracy of testability evaluation,a testability integrated evaluation method is proposed in this paper based on testability virtual test data. Considering the characteristic of testability virtual test data,the credibility analysis method for testability virtual test data is studied firstly. Then the integrated calculation method is proposed fusing the testability virtual and physical test data. Finally,certain helicopter heading and attitude system is presented to demonstrate the proposed method. The results show that the testability integrated evaluation method is feasible and effective.
Virtual testability demonstration test has many advantages,such as low cost,high efficiency,low risk and few restrictions.It brings new requirements to the fault sample generation.A fault sample simulation approach for virtual testability demonstration test based on stochastic process theory is proposed.First,the similarities and differences of fault sample generation between physical testability demonstration test and virtual testability demonstration test are discussed.Second,it is pointed out that the fault occurrence process subject to perfect repair is renewal process.Third,the interarrival time distribution function of the next fault event is given.Steps and flowcharts of fault sample generation are introduced.The number of faults and their occurrence time are obtained by statistical simulation.Finally,experiments are carried out on a stable tracking platform.Because a variety of types of life distributions and maintenance modes are considered and some assumptions are removed,the sample size and structure of fault sample simulation results are more similar to the actual results and more reasonable.The proposed method can effectively guide the fault injection in virtual testability demonstration test.
Testability plays an important role in improving the readiness and decreasing the lifecycle cost of equipment. Testability demonstration and evaluation is of significance in measuring such testability indexes as fault detection rate(FDR) and fault isolation rate(FIR), which is useful to the producer in mastering the testability level and improving the testability design, and helpful to the consumer in making purchase decisions. Aiming at the problems with a small sample of testability demonstration test data(TDTD) such as low evaluation confidence and inaccurate result, a testability evaluation method is proposed based on the prior information of multiple sources and Bayes theory. Firstly, the types of prior information are analyzed. The maximum entropy method is applied to the prior information with the mean and interval estimate forms on the testability index to obtain the parameters of prior probability density function(PDF), and the empirical Bayesian method is used to get the parameters for the prior information with a success-fail form. Then, a parametrical data consistency check method is used to check the compatibility between all the sources of prior information and TDTD. For the prior information to pass the check, the prior credibility is calculated. A mixed prior distribution is formed based on the prior PDFs and the corresponding credibility. The Bayesian posterior distribution model is acquired with the mixed prior distribution and TDTD, based on which the point and interval estimates are calculated.Finally, examples of a flying control system are used to verify the proposed method. The results show that the proposed method is feasible and effective.
