Research On Optimization Design And Integrated Evaluation Of Testability Verification Test For Equipments

Testability is a critical technical index of the equipment development and purchase. The testability verification test and evaluation are mainly intend to test and evaluate the equipment testability resulting from the design and manufacturing and are the foundation to the equipment purchase management and scientific decision making. How to implement a study on the optimization for the testability verification test and integrated evaluation method considering the limitation of test cost and period and make a low-risk verification conclusion and a high confidence evaluation conclusion is a pending problem in the theory and engineering. It relates to many physical interest and practical applications.This paper is aiming to solve the problems of high cost, long period, high-risk verification/evaluation conclusion and low accuracy/confidence level in the testability verification test and evaluation , which takes the fault detection rate (FDR) and fault isolation rate (FIR) as the specific verification and evaluation index. The systematic scheme for design of testability verification test and integrated evaluation is put forward, which is based on data in whole life period.Then, the failure sampling optimization, failure injection efficiency in the design of testability verification test and testability integrated evaluation methods are studied systematically, and the cases are analyzed to verify the effectiveness of all the methods.The major contents and conclusions of the dissertation are as follows.1. The model of relationship between the plan of sampling and the risk/confidence level of FDR/FIR verification conclusion is established, the relationship between the failure sampling structure and the risk/confidence level is analyzed , the model of relationship between the failure detection/isolation data number and the accuracy/confidence level of FDR/FIR integrated evaluation conclusion is built up by deeply analyzing of the factors which affect the risk, accuracy and confidence level of the testability verification test and evaluation. The models and the natural relationship described can provide guidance for the further pertinent study of testability verification test optimization design and integrated evaluation.2.To settle the problems such as testability verification test unable to perform due to the oversized failure sampling determined by typical plan of sampling, the low confidence level of the testability verification test conclusion resulting from the irrational failure sampling structure and the preferred failure sampling set's represention for the failure mode set of UUT based on random sampling can not be judged, the failure sample optimization selection method for the design of testability verification test is researched by determining the optimized failure sampling size , building the rational failure sampling structure and failure sampling sets evaluation and optimization. What has been investigated can offer us a new idea and method for the failure sample optimization selection of testability verification test.(1)For the determination of optimized plan of sampling, An optimized plan of sampling which is put forward based on the prior equivalent binomial data during the development stage. The study shows the failure sampling size can be efficiently reduced providing the little risk change of the producer/user and the risk of the producer/user efficiently reduced providing no change of failure sampling size.(2)For the gross error of random sampling of the failure rate-based failure sampling size resulting from the inaccurate prior failure rate, The influencing factors to error of random sampling of the failure rate-based failure sampling size allocation model are analyzed. On such basis, a Gamma distribution-based failure rate estimation method and a Bootstrap maximum likelihood failure rate estimation method are researched, so as to make a failure sampling size allocation distribution on failure rate and equipment complexity basis.The study says a more rational failure sampling structure can be obtained by the failure sampling size allocation on such basis and the error of random sampling can be decreased effectively.(3)For the description and sampling of propagation failure, a fuzzy probability Petri net is built to descript the propagation of failure and the fuzzy probability Petri net inference algorithm is added on the fuzzy probability Petri net to get the failure propagation intensity.On such basis, a random selective sampling algorithm is put forward based on the failure propagation intensity. The study indicates the random selective sampling algorithm can play a key role for the reduction of the final risk of the user.(4)Considering the problems such as evaluate the various failure sampling sets randomly sampled, a evaluation index system is founded and quantified weighing the representation of the failure sampling set to the failure mode set, on such basis the model and method for failure sample set evaluation are put forward. The study indicates the preferred failure sampling set can accurately represent the failure mode set of the subject to be tested.3.For the incapability of effective injection of failure resulting from the inaccessible location, by deepening the analysis on the major factors affecting the effectiveness of the failure injection, the failure propagation characteristic-based failure model and such model-based injection method of failure resulting from the inaccessible location are researched in light of the failure propagation characteristic. The study clarifies the failure propagation characteristic-based failure injection method effectively works out the injection incapability of failure resulting from the inaccessible location. At the same time, the fault injection cost can be considerably cut down. What has been researched can relate to many theory interest and practical applications.4.For problems in the FDR/FIR evaluation occurred when the field usage data for testability of equipment with high reliability is small sample, the Bayes Inference on Dynamic Population-based FDR/FIR integrated evaluation model and method are considered and launched. To comprehensively utilize the plentiful module test information and expert information in the equipment development stage, the pertinent information fusion method is advanced according to various information types. With the multivariate Dirichlet distribution as the prior distribution and the complete fusion of growth test data in development stage and field usage data, the FDR/FIR integrated evaluation model based Bayes inference on dynamic population is set up. An analytical method and Markov Chain Monte Carlo method are adopted to solve the complex posterior distribution inference problem. The model robustness is investigated. The study shows the FDR/FIR integrated evaluation by means of such model can produce the evaluation conclusion with high confidence level in a short field usage period or under"small sample"condition. What has been investigated can offer us an important theory base and method for testability integrated evaluation of equipments.5.A system for optimization of testability verification test and integrated evaluation is developed and take a missile control system as the subject of application. The system furnishes a useful tool for the optimization of testability verification test and integrated evaluation of large scale and complicated equipment.