压力室故障诊断模型设计基于细分控制和自适应模糊神经网络
1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, People's Republic of China. nanzhang@stu.xjtu.edu.cn.
Scientific reports
|November 29, 2024
概括
本研究提出了一种新的EWTLM-FNN框架,用于诊断飞机压力室故障. 该模型达到90%以上的准确性,通过先进的信号处理和深度学习提高飞行安全.
科学领域:
- 航空航天工程 航空航天工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 高空飞行越来越常见,需要采取强有力的安全措施.
- 飞机压力室的准确故障诊断对于飞行安全至关重要.
- 现有的方法难以应对压力室故障检测的复杂性.
研究的目的:
- 为飞机压力室引入先进的故障诊断模型.
- 为了提高压力室故障识别的精度和可靠性.
- 为未来飞机压力故障诊断系统提供基础.
主要方法:
- 开发了一个经验波纹转换-长期短期记忆网络-模糊神经网络 (EWTLM-FNN) 框架.
- 应用于气压数据的除尘和过.
- 用实证波形变换 (EWT) 进行频域分析.
- 采用三层长短期记忆网络 (LSTM) 进行特征提取.
- 集成了一个模糊的神经网络 (FNN) 用于最终的故障诊断.
主要成果:
- 该EWTLM-FNN框架显示出卓越的故障诊断性能.
- 在自制压力室故障数据集上实现了超过90%的诊断准确性.
- 在所有测试的指标中表现优于传统的机器学习和单个深度学习模型.
结论:
- 该EWTLM-FNN框架为飞机压力室故障诊断提供了一个高度准确和可靠的解决方案.
- 拟议的模型在确保飞行安全方面取得了重大进展.
- 这项研究为航空航天故障检测系统的未来发展奠定了基础.
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