Equipment battlefield repair is an important part of equipment support work. Traditional equipment battlefield repair has not met the needs of modern warfare. To improve the scientific and decision-making efficiency of equipment battlefield repair under information conditions， a generalized stochastic Petri net （GSPN） is used to model and analyze the equipment battlefield repair process， and the validity of the model is judged by invariants. According to the isomorphism characteristics of the generalized stochastic Petri net and Markov chain （ MC ）， the main performance indexes of the model are solved. Finally， through the example analysis， this method can effectively solve the main performance indexes of equipment battlefield repair， verify the validity and rationality of the model， and also provide a certain reference for equipment battlefield repair decisions.

In the testability verification test of electronic equipment， due to the high degree of circuit integration， the fault mode may be caused by one or more failure mechanisms， and traditional means such as thermal failure analysis can no longer effectively analyze the deep fault mode of electronic equipment， which leads to the reduction of the credibility of test samples. An improved fault mode analysis method based on the combination of failure physics and fault tree is proposed. Taking the overvoltage protection circuit in the power module of an electronic equipment as an example， the failure physics model is used to calculate the failure rate and criticality from the device level failure mode analysis， and the FMECA results of the overvoltage protection circuit are obtained. Guided by the fault tree analysis method， the FMECA results are verified by comparing the priority of the two according to the similarity between the definition of the probability importance of the bottom event and the criticality of the electronic components. According to this method， the fault mode analysis results of electronic equipment are finally obtained with high accuracy， which effectively improves the reliability of testability verification test samples.