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Chapter 18 Turbofan Engine Overhaul Quality Evaluation Based on Cloud Theory

机译:第18章基于云理论的涡轮机发动机大修质量评价

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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优质云模型。发动机大修质量评估系统由发动机稳定推力和发动机压力比(EPR)下发动机消耗试验条件下的发动机性能的九个参数建立。同时,将发动机大修参数的五次测试定量数据记录到云模型中的定性数据转换为定性数据。通过信息熵理论的方法给出了计算的参数的权重值。因此,作为发动机大修质量的评估标准,云重力中心加权偏差度。选择有关涡轮机发动机特伦特700的大修测试数据以验证模型。本文的结果表明,计算的性能偏差度分别为0.5188,0.4851和0.5288。第一和第三值几乎等同,而第二个值与其他两个值的比较较低。至于两者,两台发动机都配备在同一架飞机上。因此,纸上提出的云模型可以应用于精确地对发动机性能进行评估。 Aero-Engine质量评估的准确性进一步提高。结果可以为发动机舰队管理提供参考。

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