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燃气涡轮发动机故障诊断的人工神经网络法 总被引:8,自引:1,他引:7
介绍了人工神经网络专家系统在航空涡轮风扇发动机故障诊断上的应用。通过一个高涵道比涡扇发动机故障诊断的实例分析, 验证了三层逆传播网络的可行性。非常令人鼓舞的诊断结果证明:神经网络的模糊联想特点、分布存储和并行处理能力以及对随机误差的抑制作用, 使其成为一个有效的和快速的方法。它有很好的应用前景, 并可广泛用于航空发动机的故障诊断。 相似文献
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BP神经网络在结构边界参数识别中的应用 总被引:1,自引:0,他引:1
针对建立发动机动力学模型过程中,试车台机架结构边界环境的不确定状况,对神经网络在边界刚度识别中的应用进行了研究。以结构模态频率为网络输入,边界X、Y、Z方向的刚度为输出,通过一种增加训练样本的方法大大提高了网络的映射性能,最终的识别结果达到了预期目标,满足工程需要。 相似文献
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最大割问题(Max—eulProblem)是一个典型的NP难组合优化问题。文章采用遗传算法、分布估计算法、Hopfield网络方法、蚁群算法、粒子群算法等5种算法对最大割问题进行求解,并用标准的多个不同规模最大割测试数据进行测试,研究各参数对算法的影响,并比较各种算法的时间复杂度和空间复杂度。测试结果表明该五种算法虽然在执行效率上有差异,但都能较好的解决最大割问题。 相似文献
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Suren Chilingaryan Ashot Chilingarian Varuzhan Danielyan Wolfgang Eppler 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2009
Huge magnetic clouds of plasma emitted by the Sun dominate intense geomagnetic storm occurrences and simultaneously they are correlated with variations of spectra of particles and nuclei in the interplanetary space, ranging from subtermal solar wind ions till GeV energy galactic cosmic rays. For a reliable and fast forecast of Space Weather world-wide networks of particle detectors are operated at different latitudes, longitudes, and altitudes. Based on a new type of hybrid particle detector developed in the context of the International Heliophysical Year (IHY 2007) at Aragats Space Environmental Center (ASEC) we start to prepare hardware and software for the first sites of Space Environmental Viewing and Analysis Network (SEVAN). In the paper the architecture of the newly developed data acquisition system for SEVAN is presented. We plan to run the SEVAN network under one-and-the-same data acquisition system, enabling fast integration of data for on-line analysis of Solar Flare Events. An Advanced Data Acquisition System (ADAS) is designed as a distributed network of uniform components connected by Web Services. Its main component is Unified Readout and Control Server (URCS) which controls the underlying electronics by means of detector specific drivers and makes a preliminary analysis of the on-line data. The lower level components of URCS are implemented in C and a fast binary representation is used for the data exchange with electronics. However, after preprocessing, the data are converted to a self-describing hybrid XML/Binary format. To achieve better reliability all URCS are running on embedded computers without disk and fans to avoid the limited lifetime of moving mechanical parts. The data storage is carried out by means of high performance servers working in parallel to provide data security. These servers are periodically inquiring the data from all URCS and storing it in a MySQL database. The implementation of the control interface is based on high level web standards and, therefore, all properties of the system can be remotely managed and monitored by the operators using web browsers. The advanced data acquisition system at ASEC in Armenia was started in November, 2006. The reliability of the multi-client service was proven by continuously monitoring neutral and charged cosmic ray particles. Seven particle monitors are located at 2000 and 3200 m above sea level at a distance of 40 and 60 km from the main data server. 相似文献
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Yang Hai a b Cheng Wei a Zhu Hong c a Institute of Solid Mechanics Beijing University of Aeronautics Astronautics Beijing China bNo. Troop of People’ s Liberation Army Shenyang China cLiaoning Equipment Manufacture College of Vocational Technology Shenyang China 《中国航空学报》2008,21(5):423-432
Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive (TVAR) model based on the process neural network (PNN) and the empirical mode decomposition (EMD) is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions (IMFs). Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution. 相似文献