轴承剩余使用寿命的转移预测方法基于不同工作条件下的深度特征评估
Yongzhi Liu1, Yisheng Zou2, Kai Zhang3
1School of Mining and Mechanical Engineering, Liupanshui Normal University, Liupanshui 553004, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究引入了一种新的方法,用于使用特征评估和深度转移学习来预测轴承的剩余使用寿命 (RUL). 该方法通过更好地分析特征并适应不断变化的工作条件来提高准确性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 预测性维护是指预测性维护.
背景情况:
- 目前用于承载剩余使用寿命 (RUL) 预测的深度学习模型在特征分析能力上表现出局限性,并且对工作条件的变化敏感.
- 现有模型中的特征评估主要依赖于预测准确性,这提供了对特征分析能力的有限理解.
研究的目的:
- 为轴承RUL提出一个改进的预测方法,解决现有的深度学习模型的局限性.
- 提高提取特征的分析能力,在不同的工作条件下提高预测准确度.
主要方法:
- 基于趋势一致性指数的新型特征评估和选择方法被设计用于轴承寿命预测.
- 开发了一个利用特征条件映射的域对抗性转移模型,以减轻不断变化的工作条件对预测准确性的影响.
主要成果:
- 与现有的轴承评估和预测技术相比,提出的方法显示出更高的性能.
- 实验结果验证了特征评估和深度转移学习方法在提高RUL预测方面的有效性.
结论:
- 开发的方法为轴承RUL预测提供了更强大,更准确的解决方案,特别是在动态的操作环境中.
- 这项研究通过改进的特征分析和转移学习技术,有助于推进预测性维护策略.
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