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Chapter 18 Turbofan Engine Overhaul Quality Evaluation Based on Cloud Theory

机译:第十八章基于云理论的涡扇发动机大修质量评估

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The paper was aimed to construct the engine overhaul quality cloud model on the basis of the randomness and fuzziness of overhaul test data. The engine overhaul quality evaluation system was established by nine parameters of engine performances at the condition of the engine taking-off test under engine stable thrust and engine pressure ratio (EPR). In the meantime, the test quantitative data recording five times of engine overhaul parameters were transformed to the qualitative data in the cloud model. The weight values of the calculated parameters were given by method of the information entropy theory. The cloud gravity center weighted deviation degree was accordingly given as an evaluation criterion of the engine overhaul quality. The overhaul test data concerning turbofan engine TRENT 700 were chosen in order to validate the model. The results of the paper show that the calculated performance deviation degree was separately 0.5188, 0.4851, and 0.5288. The first and third values were nearly equivalent, while the second one was lower in comparisons with the other two values. As for the two former, the two engines were equipped on the same airplane. Therefore, the cloud model proposed in the paper can be applied to accurately make assessments of the engine performances. The accuracy of the aero-engine quality evaluation is further improved. The results can provide the references for the engine fleet management.
机译:本文旨在基于大修测试数据的随机性和模糊性,构建发动机大修质量云模型。通过在发动机稳定推力和发动机压力比(EPR)下进行发动机起飞试验的条件下,通过发动机性能的九个参数建立了发动机大修质量评估系统。同时,将记录了五次发动机大修参数的测试定量数据转换为云模型中的定性数据。利用信息熵理论给出计算参数的权重值。因此,给出了云重心加权偏差度作为发动机大修质量的评价标准。选择了有关涡扇发动机TRENT 700的大修测试数据,以验证模型。纸的结果表明,计算出的性能偏差度分别为0.5188、0.4851和0.5288。第一个和第三个值几乎相等,而第二个与其他两个值相比要低一些。至于前两个,两个发动机都安装在同一架飞机上。因此,本文提出的云模型可用于准确评估发动机性能。航空发动机质量评估的准确性进一步提高。研究结果可为发动机机队管理提供参考。

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