剩余结构的应用 基于注意力机制的时间卷积网络在剩余的使用寿命间隔中 轴承的预测
Chunsheng Zhang1,2, Mengxin Zeng3, Jingjin Fan4
1SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, China.
一个新的模型,TCN-AM-GPR,准确地预测轴承的寿命,并量化不确定性. 这种方法改进了现有的方法,为工业机械提供了可靠的剩余使用寿命 (RUL) 估计,并提供了可靠的可信度区间.
科学领域:
- 机械工程和工业物联网的工业物联网.
- 机器学习用于预测性维护
背景情况:
- 轴承是工业4.0机械的关键部件,需要精确的健康状况监测,以确保操作可靠性.
- 目前的轴承寿命预测模型往往缺乏不确定性量化,需要改进准确性.
研究的目的:
- 开发一种新型模型,以准确的长期预测轴承的剩余使用寿命 (RUL),并量化不确定性.
- 提高工业环境中轴承寿命预测的准确性和可靠性.
主要方法:
- 拟议的TCN-AM-GPR模型利用从传感器信号的时空图形构造.
- 整合了基于注意力机制的软值决策残余结构,以减少信号噪声.
- 用于RUL和置信区间估计的区间量化层.
主要成果:
- 与TCN-AM-GPR相比,TCN-AM-GPR显示出优越的点预测准确度,在R2中实现了2.17%的改进.
- 该模型获得了最佳的间隔预测评估指数,与TCN-GPR相比,MWP减少了16.73%.
- 在PHM2012携带数据集上的经过验证的性能.
结论:
- TCN-AM-GPR确保了准确的点估计轴承规则.
- 该模型在描述预测不确定性方面提供了显著的优势,这对于实际的工业应用至关重要.
- 拟议的方法对于预测性维护策略具有实际意义.
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