Ordnance Technological Research Institute

Shijiazhuang, China

Ordnance Technological Research Institute

Shijiazhuang, China
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Zhang X.,Ordnance Engineering College | Huang K.,Ordnance Technological Research Institute | Yan P.,Ordnance Technological Research Institute | Lian G.,Ordnance Technological Research Institute
Journal of Systems Engineering and Electronics | Year: 2015

In order to meet the demand of testability analysis and evaluation for complex equipment under a small sample test in the equipment life cycle, the hierarchical hybrid testability modeling and evaluation method (HHTME), which combines the testability structure model (TSM) with the testability Bayesian networks model (TBNM), is presented. Firstly, the testability network topology of complex equipment is built by using the hierarchical hybrid testability modeling method. Secondly, the prior conditional probability distribution between network nodes is determined through expert experience. Then the Bayesian method is used to update the conditional probability distribution, according to history test information, virtual simulation information and similar product information. Finally, the learned hierarchical hybrid testability model (HHTM) is used to estimate the testability of equipment. Compared with the results of other modeling methods, the relative deviation of the HHTM is only 0.52%, and the evaluation result is the most accurate. © 1990-2011 Beijing Institute of Aerospace Information.

Zhang X.-S.,Ordnance Engineering College | Huang K.-L.,Ordnance Technological Research Institute | Yan P.-C.,Ordnance Technological Research Institute | Lian G.-Y.,Ordnance Technological Research Institute | Wang S.-G.,Ordnance Technological Research Institute
Hangkong Dongli Xuebao/Journal of Aerospace Power | Year: 2014

In order to overcome non-standard prior information processing and poor result credibility during the testability evaluation under small sample test, the method of confirming system testability prior parameters value from expert experience information, subsystem testability information and virtual simulation information was investigated. The testability parameters' prior value was estimated by the weighted fuzzy uncertainty method, subsystem data conversion and Dempster-Shafer (D-S) evidence fusion method based on the similarity measure respectively, according to different kinds of testability prior information. The example analysis shows that the prior parameters value during testability evaluation by the proposed method are lower than that of methods from literatures about 0.8%.

Zhang X.,Ordnance Engineering College | Huang K.,Ordnance Technological Research Institute | Yan P.,Ordnance Technological Research Institute | Lian G.,Ordnance Technological Research Institute | Wang S.,Ordnance Technological Research Institute
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | Year: 2015

It is of great importance to useany prior information effectively and reasonably in the evaluation of small samples. Therefore, a new testability evaluation method based on mixed Beta prior distribution is presented, while considering both the credibility and the importance of prior information as well as the testability evaluation of complex equipment in small samples. The results show that, according to classical methods using small binomial samples, the lowerconfidence limits of product testability are conservative. Most of the measurements for the credibility of the prior information are based on data. The evaluation results are aggressive due to the missing sources of prior information. Thus, the conclusion is reasonable, and this method is promising for engineering applications. ©, 2015, Nanjing University of Aeronautics an Astronautics. All right reserved.

Jin S.S.,Shijiazhuang Mechanical Engineering College | Huang K.L.,Ordnance Technological Research Institute | Lian G.Y.,Ordnance Technological Research Institute | Li B.C.,Shijiazhuang Mechanical Engineering College
Applied Mechanics and Materials | Year: 2013

For the problems of not enough fault information for the complicated equipment and difficult to predict the fault, we apply Support Vector Machine(SVM)to build the fault prediction model. On the basis of analyzing regression algorithm of SVM, we use Least Square Support Vector Machine(LS-SVM)to build the fault prediction model.LS-SVM can effectively debase the complication of the model. Finally, we take the fault data of a hydraulic pump to validate this model. By selecting appropriate parameters, this model can make better prediction for the fault data, and it has higher prediction precision. It is proved that the fault prediction model which based on LS-SVM can make better prediction for fault trend of complicated equipment. © 2013 Trans Tech Publications Ltd, Switzerland.

Yang H.-H.,Ordnance Engineering College | Yang H.-H.,Ordnance Technological Research Institute | Liu F.,Ordnance Technological Research Institute | Feng W.-L.,China Institute of Metrology
Jiliang Xuebao/Acta Metrologica Sinica | Year: 2016

In view of the quality problem of metrological grating Moiré fringe signal, a method based on empirical mode decomposition (EMD) algorithm is presented for non-stationary metrological grating Moiré fringe signal de-noising. A non-stationary factual dynamic grating signal model was established. The simulation experiment of filtering analysis was conducted for several grating signal models with different noise by using the EMD algorithm advantages which is not required to define the filter parameters. The two indicators of SNR and RMSE shown that the EMD filtering outperforms the median and wavelet threshold methods. Add the high-order harmonic components to the sine & cosine ideal signals and the ideal compensation results of sine deviation error in metrological grating Moiré fringe signal de-noised processing can be verified by comparing before and after Lissajous figures of suppressing the high-order harmonics with EMD algorithm. © 2016, Acta Metrologica Sinica Press. All right reserved.

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