使用可逆神经网络的可解释性故障诊断-第一部分:基于左边多重组的解决方案.
IEEE transactions on neural networks and learning systems
|September 5, 2024
概括
本研究介绍了一种使用可逆神经网络 (INN) 进行非线性反控制系统的智能故障诊断 (FD) 方法. 这种新的方法增强了系统识别,避免了过度匹配,提供了可解释的学习过程.
科学领域:
- 控制工程 控制工程 控制工程
- 机器学习 机器学习
- 系统识别系统识别系统
背景情况:
- 非线性反控制系统对传统故障诊断 (FD) 方法构成挑战.
- 现有的FD方法在学习复杂的非线性动态时可能会遇到过度拟合的问题.
- 对于这些系统,需要可解释和专门的FD技术.
研究的目的:
- 为非线性反控制系统开发一种新的智能故障诊断 (FD) 范式.
- 设计一个基于反向神经网络 (INN) 的FD方案,使用左.
- 为了提高系统识别准确度,避免在非线性动态中出现过拟合问题.
主要方法:
- 作为系统数据投射到零空间上的残余发生器的构造.
- 在拓空间中,在系统输出和残余之间的可逆关系中,制定一个同态.
- 引入主和奴隶目标函数,用于无损信息的系统/参数识别.
主要成果:
- 通过两项非线性系统研究,证明了基于可逆左方形 (ILM) 的FD策略的可行性.
- 拟议的基于INN的FD方案有效地避免了非线性系统动态中的过问题.
- 以控制理论为指导的设计确保了学习过程的可解释性.
结论:
- 开发的基于INN的FD范式为非线性反控制系统提供了专门和有效的解决方案.
- 这项研究有助于进步基于机器学习的系统识别和可解释的FD.
- 这些发现为未来的研究铺平了道路,包括在第二部分的基于右集群的FD设计.
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