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Fault evaluation method of flap asymmetry based on a multi-state neural network
编号：172 访问权限：仅限参会人 更新：2026-10-19 18:06:40 浏览：3次 口头报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

所在会场：[暂无会议] [暂无会议段]

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摘要
A gap between health status, function, and performance during the evaluation process leads to a divide between reliability experts and system designers in their work schedule. Given this issue, an evaluation method for flap asymmetry fault based on a multi-state neural network is established in this study. First, the flap asymmetric fault localization is determined as per the structure and composition of the FAS. Second, considering the impact of external factors on components, the proposed multi-state neural network method is employed to divide traditional fault states into fault and pseudo-fault. By introducing performance anomaly and external factors anomaly, function is linked with performance during the health status assessment. Furthermore, an accuracy model is developed for multi-state neural network. Finally, the method for analyzing flap asymmetry fault of flaps is verified by example. The results reveal that the function and performance are closely associated with health status. After optimization, the accuracy of sample points is increased by up to 9%, verifying the reliability of the evaluation method.
 
关键词
FAS; Flap asymmetry; Fault evaluation; multi-state neural network
报告人
Yi Li
RoleCatcher AVIC Qing’an GroupCo. , Ltd.,

稿件作者
Yi Li AVIC Qing’an GroupCo. , Ltd.,
Zhaowei Lang AVIC Qing’an GroupCo. , Ltd.,
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重要日期

  • 会议日期

    11月06日

    2026

    至

    11月08日

    2026

  • 10月15日 2026

    初稿截稿日期

主办单位
IEEE Instrumentation and Measurement Society
承办单位
Sichuan University
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