一个知识-数据集成框架,用于在整个生命周期内预测滚动元件轴承的RUL
Lei Yang1, Tuojian Li1, Yue Dong1
1Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049, China; Shaanxi Key Laboratory of Mechanical Product Quality Assurance and Diagnostics, Xi'an Jiaotong University, Xi'an 710049, China.
ISA transactions
|July 10, 2024
概括
本研究引入了一种新的框架,通过有效地整合多传感器数据并使生命周期预测成为可能,来预测滚动元件轴承的剩余使用寿命 (RUL). 拟议的方法显著提高了跨不同数据集的预测准确性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 预测和健康管理 (PHM)
背景情况:
- 对滚动元件轴承 (REB) 的准确剩余使用寿命 (RUL) 预测对于工业维护和运营效率至关重要.
- 现有的方法在多传感器数据利用,模型解释性和完整生命周期预测准确性方面面临挑战.
- 当前RUL预测中的瓶限制了其在现实应用中的有效性.
研究的目的:
- 开发一个基于知识的,可解释的框架,用于准确的,REBs的生命周期RUL预测.
- 解决当前模型在处理多传感器数据和预测整个操作寿命的局限性.
- 提高RUL预测模型的可靠性和可解释性.
主要方法:
- 一个改进的图形卷积自编码器,包含皮尔森相关性和特征转换,用于快速变化的信号融合.
- 一个级联式多头自我注意力自编码器,具有多源信号集成和健康指标构建的特征指导.
- 使用连续梯度识别与异常检测的生命周期阶段划分,加上基于测量的校正寿命公式和双向递归门式双重注意力单元.
主要成果:
- 与现有模型相比,拟议的框架显示了RUL预测准确度的显著改进.
- 对于自行设计的测试装置,修正累积相对精度 (CCRA) 提高了1.66%,确定系数 (R2) 提高了49.00%.
- 在PHM 2012数据集中,该框架导致CCRA增加了4.04%,R2.2%增加了120.72%.
结论:
- 开发的基于知识的可解释框架有效地克服了多传感器数据利用和生命周期RUL预测的局限性.
- 拟议的特征融合,健康指标构建和生命周期细分的方法导致了优越的预测性能.
- 这一框架为滚动元件轴承的准确和可解释的RUL预测提供了一个有希望的解决方案.
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