Deep clustering variational network for helicopter regime recognition in HUMS

Jingyao Wu; Chuang Sun; Chi ZhangXuefeng ChenRuqiang Yan

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Deep clustering variational network for helicopter regime recognition in HUMS

机译：Deep clustering variational network for helicopter regime recognition in HUMS

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摘要

The helicopter health and usage monitoring system is the core system to ensure its operational safety. Flight regime recognition is a key pilot task therein that affects subsequent decision-making. However, the current research on this topic has not aroused wide attention. Taking advantage of deep learning, a powerful pattern recognition tool, we proposed a deep clustering variational network to serve the helicopter regime recognition task. Through explicit feature distribution constraints and clustering loss function, we have made a clearer decision boundary and more significant category differences, thus achieving accurate recognition results. Two case studies show that deep clustering variational network can effectively recognize the regimes by utilizing vibration signals in time between overhaul experiments or online flight parameters.

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来源
《Aerospace science and technology》 |2022年第5期|107553.1-107553.13|共13页
作者
Jingyao Wu; Chuang Sun; Chi ZhangXuefeng ChenRuqiang Yan;
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作者单位

School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类
关键词
Helicopter; Regime recognition; Health and usage monitoring system; Deep learning;

Deep clustering variational network for helicopter regime recognition in HUMS

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