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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Fault Detection for Turbine Engine Disk Based on One-Class Large Vector-Angular Region and Margin
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Fault Detection for Turbine Engine Disk Based on One-Class Large Vector-Angular Region and Margin

机译：基于一类大矢量角区域和裕量的涡轮发动机盘故障检测

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摘要

Fault detection is an important technique to detect divergence based on unknown abnormalities, which involves establishing a computational model exclusively originated from the key features of the normal samples. The multimodality of process data distribution of engine turbine disk is inevitably affected by incorporation of ambient disturbance; the mean and covariance would vary significantly, resulting in decayed detecting accuracy. By adopting a strategy to maximize vector-angular mean and minimize vector-angular variance simultaneously in the feature space, a one-class large vector-angular region and margin (one-class LARM) framework is systematically conducted for fault detection of turbine engine disk which will enhance the robustness of the dynamic multimode process monitoring. Simulation based on the single mode and multimode of turbine engine disk is thoroughly performed and compared that the results of which solidly validated the favorable efficiency of the proposed method.
机译：故障检测是一种基于未知异常检测发散的重要技术，它涉及建立完全源自正常样本关键特征的计算模型。发动机涡轮盘过程数据分布的多模态性不可避免地受到环境扰动的影响;均值和协方差将发生显著变化，导致检测精度下降。通过采用特征空间中矢量角均值最大化和矢量角方差同时最小化的策略，系统地构建了涡轮发动机盘片故障检测的一类大矢量角区域和余量（一类LARM）框架，增强了动态多模式过程监测的鲁棒性。基于涡轮发动机盘的单模和多模仿真进行了深入的仿真，并进行了对比，结果充分验证了所提方法的良好效率。

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