适应性中间阶级智能分布对齐:机器故障诊断的通用域适应和通用化方法
IEEE transactions on neural networks and learning systems
|March 21, 2024
概括
本研究介绍了用于故障转移诊断的自适应中间阶级智能分布对齐 (AICDA) 模型. 通过在不需要手动调整的情况下在多个领域对齐分布,AICDA提高了诊断准确性和概括性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 机械工程 机械工程
背景情况:
- 对于故障诊断的转移学习方法通常需要针对特定任务的参数调整.
- 现有的域调整机制受到动态调整目标的影响,导致不稳定性和不良稳定性.
研究的目的:
- 提出一种新而简单的转移学习诊断方法,即适应性中间阶级智能分布对齐 (AICDA) 模型.
- 克服现有方法的局限性,包括参数调整和动态对齐问题.
主要方法:
- 开发了AICDA机制,用于跨多个领域的适应性中间分布对齐.
- 引入了一个动态中间对齐 (DIA) 适应层,用于在没有显式分布损失的情况下进行域混.
- 利用AdaSoftmax损失来提高分类性能和可分离性.
主要成果:
- AICDA模型同时对源域和目标域的全球和类型分布进行了对齐.
- DIA 层以适应性实现域混,减少对分布距离和规范化损失的依赖.
- 对风力轮机行星变速箱的实验结果表明,与最先进的方法相比,诊断准确度和概括能力更高.
结论:
- AICDA模型为多源混合故障转移诊断提供了强大而有效的解决方案.
- 拟议的方法在域调整和域泛化任务中表现出强大的泛化能力.
- 在复杂的机械系统中,AICDA显著提高了故障诊断性能.
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