在可变的工作条件下进行滚动轴承故障诊断的强化组合学习方法
Yanning Li1,2, Yi Zhang1,2, Ruixin Wang3
1School of Automation, Northwest Polytechnic University, Xi'an 710072, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了一种新的增强组合方法,用于在不同条件下准确的滚动轴承故障诊断. 该方法增强了机器诊断,并提供了实际的工程效益.
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:
- 准确的故障诊断对于滚动轴承的运行至关重要.
- 在不同的工作条件下识别故障类型是一个重大的工程挑战.
研究的目的:
- 提出一种强化组合方法,用于在不同工作条件下诊断滚动轴承故障.
主要方法:
- 一个强化模型被设计出来,以选择最佳的基础学习者.
- 分层随机抽样提取了四个数据集用于培训.
- 一个稀疏的人工神经网络 (ANN) 被设计为组合模型.
主要成果:
- 通过强化学习获得了四个最佳基础学习者.
- 构建的强化学习模型在可变条件下成功识别了故障类型.
- 实验证明了该方法在其他智能方法上的优越性.
结论:
- 拟议的钢筋组合方法为滚动轴承故障诊断提供了显著的实际工程益处.
- 该方法在不同的工作条件下识别故障类型时达到很高的准确性.
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