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使用ISMA进行故障诊断,以优化飞机发动机损坏修复的SVM参数

Peng Xue¹, Jie Yu², Hongwei Zhang³

¹Engineering Techniques Training Center, Civil Aviation University of China, Tianjin, 300300, China. pxuecauc@163.com.

Scientific reports

|November 26, 2025

概括

本研究介绍了一种先进的航空发动机状态监测系统,使用自动编码器 (AE) 和双向门循环单元 (BiGRU) 来进行特征提取. 该方法显著提高了故障诊断的准确性和实时性能.

科学领域:

航空航天工程航空航天工程
机械工程机械工程
人工智能的人工智能

背景情况:

航空发动机需要强大的状态监控 (CM) 以确保安全和效率.
目前的CM信号故障诊断方法的准确性低,实时能力差.
在复杂的发动机环境中识别微妙的故障信号是一个重大挑战.

研究的目的:

为增强航空发动机CM信号故障诊断开发一个综合模型.
提高航空发动机故障检测的准确性和实时性能.
利用深度学习和优化算法进行卓越的故障识别.

主要方法:

提出了一个AE-BiGRU模型,用于有效地从CM信号中提取特征.
引入了改进的粘液模具算法 (ISMA),以优化支持矢量机 (SVM) 参数进行分类.
在C-MAPSS数据集上验证了模型的性能.

主要成果:

在功能优化方面,ISMA证明了优越的差异减少.
该AE-BiGRU-ISMA-SVM模型实现了高精度 (0.90),回忆 (0.95) 和F1得分 (0.92).
该模型在提取弱故障信号和识别故障频率方面表现出色.

结论:

拟议的AE-BiGRU-ISMA-SVM方法显著提高了航空发动机故障诊断.
这种方法可以改善发动机健康状况的保护,并有助于及时修复损坏.
综合模型为航空业的实时,准确的CM提供了一个有前途的解决方案.

关键词:

飞机发动机飞机发动机飞机发动机错误诊断是一个错误的诊断.操作监控操作监控操作监控信号的特征信号的特征粘模具算法粘模具算法支持矢量机器的支持矢量机器.

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