A practical mathematic model for in time correction-based testability growth test is proposed in this article. This model can make up the gap between the testability growth test practice and theory. The process of testability design defects identification and correction is formulated, based on which the testability growth test plan optimization method is given with the minimum test cost criterion. A Bayes approach is studied to track the testability growth from the test data. Then the tracked results are used to learn the system correction skill and to project the subsequent testability growth test. Simulation results show that the models and methods presented in this paper are reasonable and can efficiently manage the in time correction-based testability growth test planning, tracking, and projecting problem.
In this paper, we propose a Markov chain-based testability growth model (TGM) for the just in-time fix program. This model can help the system designers to manage the testability growth process during system maturation. We also derive a cost-benefit model for allocating test resources to optimize a specified testability metric subject to a constraint on cumulative test cost. Bayesian inference, coupled with a hybrid genetic and particle swarm optimization method, is used to estimate the parameters of the TGM from evolving data, and the resulting model is utilized to track and project the testability metric. A near-optimal Lagrangian relaxation-based algorithm is applied to solve the test resource allocation problem. The testability growth and resource allocation models are validated via simulation examples. Results show that the model and algorithms presented in this paper have the potential to efficiently manage the testability growth problem.
Misjudging intermittent faults as permanent faults is the major cause of the problems of false alarms, Cannot Duplication and No Fault Found in aircraft avionics. To address this problem, we propose an approach for diagnosis of PFs and IFs in the context of discrete event systems models. Since fault events are usually unobservable, it is difficult to discriminate PF from IF events captured in succeeding sensors. Thereafter, the environmental stresses are treated as fault events and a stress level evaluation algorithm based on interval grey relational degree is given to identify the fault events by evaluating the level of the correlative environmental stresses. Finally, an example of aeronautic gyroscope is presented to demonstrate the proposed approach, and the analysis results show the approach is effective and feasible. It is a novel and effective way to discriminate both PFs and IFs of discrete event systems without the assumption of knowing the fault types (permanent faults and intermittent faults) a priori.
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