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基于xLSTM-变压器神经网络的RUL预测,用于不同工作条件下的滚动元件轴承
Runzhong Jiang1, Ziqi Li1, Haiyu Lu1
1School of Mechanical Engineering, Guangxi University, Nanning 530004, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究介绍了一种使用扩展长短期存储器 (xLSTM) 和变压器的混合模型,用于在滚动轴承中准确预测剩余使用寿命 (RUL). 先进的模型通过改善轴承健康评估和减少设备停机时间来增强预测性维护.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 预测性维护依赖于对滚动轴承的准确剩余使用寿命 (RUL) 预测.
- 传统的深度学习模型在捕捉健康指标 (HI) 的短期动态和长期退化趋势方面面临挑战.
研究的目的:
- 提出一种新的混合深度学习模型,用于改进滚动轴承的RUL预测.
- 为了同时解决捕捉当地时间特征和全球退化趋势的局限性.
主要方法:
- 开发了一种混合模型,将扩展长短期内存 (xLSTM) 与变压器架构集成在一起.
- 采用了一个编码器-解码器结构,结合了多头注意力和xLSTM模块.
- 在XJTU-SY和PHM2012滚动轴承数据集上验证了模型.
主要成果:
- 拟议的模型在RMSE和R2.2等关键指标上表现优于比较方法.
- 在预测准确度和多数据集概括能力方面取得了显著的改进.
- 有效地捕获了短期动态特征和长期退化模式.
结论:
- 混合式xLSTM-变压器模型为轴承健康评估和RUL预测提供了更准确和更具普遍性的解决方案.
- 该模型显示了在工业设备中推进智能健康管理的巨大潜力.
- 这种方法提高了预测性维护策略的可靠性和效率.
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