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首页> 外文会议>ASME(American Society of Mechanical Engineers) Turbo Expo vol.1; 20070514-17; Montreal(CA) >A REAL-WORLD APPLICATION OF FUZZY LOGIC AND INLFLUENCE COEFFICIENTS FOR GAS TURBINE PERFORMANCE DIAGNOSTICS
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A REAL-WORLD APPLICATION OF FUZZY LOGIC AND INLFLUENCE COEFFICIENTS FOR GAS TURBINE PERFORMANCE DIAGNOSTICS

机译：燃气轮机性能诊断的模糊逻辑和影响系数的真实世界应用

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摘要

This paper presents an example of the use of fuzzy logic combined with influence coefficients applied to engine test-cell data to diagnose gas-path related performance faults. The approach utilizes influence coefficients which describe the changes in measurable parameters due to changes in component condition such as compressor efficiency. Such approaches usually have the disadvantages of attributing measurement noise or sensor errors to changes in engine condition, and do not have the ability to diagnose more faults than the number of measurement parameters that exist. These disadvantages usually make such methods impractical for anything but simulated data without measurement noise or errors. However, in this example, the influence coefficients are used in an iterative approach, in combination with fuzzy logic, to overcome these obstacles. The method is demonstrated using eight examples from real-world test-cell data.
机译：本文提供了一个示例，该示例结合了将模糊逻辑与影响系数应用于发动机测试单元数据来诊断与气路相关的性能故障的方法。该方法利用影响系数，该影响系数描述了由于诸如压缩机效率之类的部件条件的变化而导致的可测量参数的变化。这样的方法通常具有将测量噪声或传感器错误归因于发动机状况变化的缺点，并且不具有诊断比现有测量参数数量更多的故障的能力。这些缺点通常使得这种方法对于除了没有测量噪声或误差的模拟数据以外的任何方法都是不切实际的。但是，在此示例中，影响因子与模糊逻辑结合用于迭代方法中，以克服这些障碍。使用来自真实测试单元数据的八个示例演示了该方法。

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