气力轮机的故障检测和隔离方法采用自组织型-3模糊波纹神经网络
Nastaran Mehrabi Hashjin1, Mohammad Hussein Amiri2, Amin Beheshti3
1Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.
Scientific reports
|January 27, 2026
概括
一个新型的自组织型-3粗波形神经网络 (ST3FRWNN) 改进了燃气轮机故障检测和隔离 (FDI). 这种可适应的系统提高了现实世界的监控应用程序的可靠性和效率.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 气力轮机的数据驱动故障检测和隔离 (FDI) 对运营效率和安全至关重要.
- 现有的外国直接投资方法往往很脆弱,缺乏适应性和对噪音和不确定性的强度.
研究的目的:
- 为燃气轮机开发一个自我组织,适应性强,强大的外国直接投资系统.
- 提高FDI计划中的不确定性处理和模型紧性.
主要方法:
- 引入Type-3模糊会员功能,以改善不确定性管理.
- 混合优化使用亚当和无味卡尔曼波器用于神经模糊架构培训.
- 实现适应规则生成和修剪的自我组织机制.
主要成果:
- 在西门子燃气轮机模拟器上实现了高平均外国直接投资率:99.302%用于检测和99.324%用于隔离.
- 与最先进的模糊系统 (FSRE-AdaTSK,TSK-SRB) 相比,其表现优越,规则较少.
- 展示了与使用更少参数的深度学习模型 (变压器,LSTM,CNN) 相比的竞争性性能.
结论:
- 拟议的自组织型-3粗波形神经网络 (ST3FRWNN) 为燃气轮机健康监测提供了一个实用和可部署的解决方案.
- ST3FRWNN对噪声 (20dB SNR) 和计算效率具有出色的稳定性.
- ST3FRWNN的自适应性和自我组织性质确保了在动态的操作环境中准确和可靠的外国直接投资.
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