用动态间隔预测模型进行健康评估的新策略
Lingli Jiang1, Heshan Sheng1, Tongguang Yang2
1School of Mechanical and Electrical Engineering, Foshan University, Foshan 528000, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本研究引入了一种使用模型驱动的动态间隔方法的新型轴承寿命预测模型. 该方法在预测轴承剩余寿命方面达到90%以上的准确性,提高了机械维护.
科学领域:
- 机械工程 机械工程
- 可靠性工程可靠性工程
- 数据科学数据科学数据科学
背景情况:
- 轴承是旋转机械中必不可少的部件,准确的剩余寿命预测对于操作健康至关重要.
- 目前用于快速准确地预测轴承寿命的方法在工业和学术环境中面临着重大挑战.
研究的目的:
- 开发一种新,准确和高效的策略,以进行健康评估和预测剩余寿命.
- 通过提出基于模型的动态间隔预测模型来解决现有方法的局限性.
主要方法:
- 利用映射比例算法来识别降解阶段,以及改进的回火算法来确定预测的最佳数据间隔.
- 通过融合36个时间和频率域指标,结合轴承降解曲线和信息融合,构建了一个健康指数.
- 通过构建基于巴黎-DSSM公式的状态空间方程和使用最小间隔数据的粒子过器来代参数,开发了一个生命预测模型.
主要成果:
- 拟议的策略表明,在XJTU-SY滚动轴承寿命数据集上,轴承寿命预测的准确性超过了90%.
- 验证证实了改进的模拟回火算法在选择有限的数据间隔时的有效性.
- 使用颗粒过器重建的健康指标和更新的状态空间方程被证明是准确和有效的.
结论:
- 模型驱动的动态间隔预测模型为轴承剩余寿命预测提供了高度准确和有效的解决方案.
- 集成先进的算法,如模拟回火和颗粒过器,提高了轴承健康评估的精度.
- 这种方法为提高机械设备的维护和运行效率提供了可靠的工具.
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