功能解集成域概括网络用于在未知的操作条件下进行轴承故障诊断
Qiyang Xiao1, Maolin Yang1, Jiayuan Yan2
1School of Artificial Intelligence, Henan University, Zhengzhou, 450046, Henan, China.
Scientific reports
|December 27, 2024
概括
一个新的功能解集成域泛化网络 (FDIDG) 改善了在未知条件下轴承故障诊断. 这种方法提取了通用的故障特征,提高了在可变工程环境中深度学习模型的可靠性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现实世界工程面临着不同操作条件的挑战,导致训练和实际故障数据之间的数据分布转移.
- 这些分布转移往往导致机械设备中的深度学习诊断模型失败.
- 开发模型,将诊断知识从已知的 (源) 域推广到未见的 (目标) 域,至关重要.
研究的目的:
- 提出一个域泛化网络,特征解集成域泛化网络 (FDIDG),用于在未知的操作条件下诊断轴承故障.
- 在面对培训与现实世界故障数据之间的分布差异时,解决深度学习模型的局限性.
- 在动态的工业环境中提高诊断系统的稳定性和准确性.
主要方法:
- 开发了一个"特征解"算法,从多个源域中提取通用的故障特征表示.
- 这种算法缩小了源域的数据分布,将故障特征概括为减少它们与操作条件的合.
- 采用了多专家整合策略和域私有功能,以提高诊断准确度和减轻边缘样本的影响.
主要成果:
- 拟议的FDIDG网络在跨领域实验中展示了出色的概括能力.
- 在公共和私人数据集上进行了实验,验证了模型在不同条件下的性能.
- 功能脱和多专家集成有效地提高了在未知的操作条件下诊断的准确性.
结论:
- FDIDG网络提供了一个强大的解决方案,用于在未知操作条件的情况下进行轴承故障诊断.
- 该方法成功地提取了通用的诊断知识,克服了数据分布的差异.
- 在复杂的工程应用中,FDIDG对提高人工智能驱动诊断的可靠性显示出显著的希望.
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