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Prognostic and Health Management (PHM), which could provide the ability of fault detection (FD), fault isolation (FI) and estimation of remaining useful life (RUL), has been applied to detect and diagnose device faults and assess its health status, with aiming to enhance device reliability, safety, and reduce its maintenance costs. In this paper, taking an aircraft fuel System as an example, with virtual instrument technology and computer simulation technology, an integrated approach of signal processing method and model-based method is introduced to build the virtual simulation software of aircraft fuel PHM system for overcoming the difficulty in obtaining the failures information from the real fuel system. During the process of constructing the aircraft fuel PHM system, the first step is to analyze the fuel system failure modes and status parameters that can identify the failure modes. The main failure modes are determined as joints looseness, pipe broken, nozzle clogging, and fuel tank leakage. The status parameters are fuel pressure and fuel flow. Then, the status parameter model is constructed to imitate the behavior of sensor which detecting fuel system status. On this basis, utilizing the signal processing module provided by Labview software, the outputs from the virtual sensors, which collect the failure data, are processed to realize the simulation of failure detection and failure diagnosis. All the result shows that the virtual simulation software well accomplishes the task of the aircraft fuel system failure detection and diagnosis.

An evaluation index system is established in order to evaluate the comprehensive teaching and research efficiency of “Double First-Class” universities in China, using the Joint Data Envelopment Analysis method. The influence of the parameters in the model is analyzed and the algorithmic steps are given. The key factors of the system are identified by the Grey Correlation Analysis method. The comprehensive teaching and research efficiency of “Double First-Class” Universities in China in 2019 has thereby been calculated as an empirical study. The results show that: (1) It is effective and feasible to construct an evaluation method based on joint DEA, for the evaluation of comprehensive teaching and research efficiency of “Double First-Class” Universities in China. (2) The key factors are the number of cited papers, the annual budget, and the salaries of graduates. Based on those factors, suggestions have been put forward to improve their efficiency. (3) The comprehensive teaching and research efficiency of China’s “Double First-Class” Universities are high, and the comparison between research efficiency and teaching efficiency shows that 69.2% of the “Double First-Class” Universities have higher research efficiency than teaching efficiency. Most of those universities allocate a higher proportion of shared input to research.

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