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首页> 外文会议>7th Biennial Conference on Engineering Systems Design and Analysis 2004(ESDA 2004) vol.3: Design Engineering; Maintenance Engineering; Manufacturing Engineering; ... >IDENTIFICATION TECHNIQUE OF AIRCRAFT GAS TURBINE ENGINE'S HEALTH
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IDENTIFICATION TECHNIQUE OF AIRCRAFT GAS TURBINE ENGINE'S HEALTH

机译：飞机燃气轮机健康的识别技术

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摘要

Groundlessness of probability-statistic methods application is shown, especially at an early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the volume of the information has property of the fuzzy, limitation and uncertainty. Hence efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the fuzzy logic and neural networks methods is considered. Training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis is made. For models choice is offered the application of the fuzzy correlation analysis results. Dynamics of correlation coefficients changes is considered. At the information sufficiency it is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and nonlinear generalised models at presence of noise measured (the new recursive least squares method). As application of the given technique the estimation of the new operating aviation engine D-30KU-154 (aircraft Tu-154M) technical condition was made.
机译：当信息量具有模糊性，局限性和不确定性时，尤其是在航空燃气涡轮发动机（GTE）技术状态诊断的早期阶段，概率统计方法的应用就显得毫无根据。因此，在模糊诊断和神经网络方法的诊断阶段，考虑了新技术软计算的应用效率。在统计模糊数据的基础上，对多个线性和非线性模型（回归方程）进行了高精度的训练。对于模型的选择，提供了模糊相关分析结果的应用。考虑相关系数变化的动力学。在信息充分的情况下，提供了在存在噪声的情况下使用航空GTE技术状态识别（使用硬计算技术）的递归算法测量多个线性和非线性广义模型的输入和输出参数（新的递归最小二乘）方法）。作为给定技术的应用，对新运行的航空发动机D-30KU-154（飞机Tu-154M）的技术条件进行了估算。

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