相关实验视频
Updated: May 25, 2025
04:58
A Rapid Method for Modeling a Variable Cycle Engine
Published on: August 13, 2019
7.5K
数字双驱动的运营周期基于GAN的多个虚拟物理映射,用于在有限的生命周期数据下预测剩余的使用寿命
Quanning Xu1, Zihao Lei1, Shulong Gu1
1National Key Lab of Aerospace Power System and Plasma Technology, Xi'an Jiaotong University, Xi'an 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China; School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
Journal of advanced research
|February 26, 2025
概括
本研究介绍了使用CycleGAN生成现实的设备生命周期数据的数字双胞胎框架,克服了工业数据稀缺性,以准确预测剩余使用寿命 (RUL).
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的剩余使用寿命 (RUL) 预测对于制造业的运行安全和可靠性至关重要.
- 不够高质量的生命周期数据阻碍了智能RUL预测算法的开发.
- 数字双胞胎技术通过创建物理资产的虚拟表示,为数据稀缺提供了一个有希望的解决方案.
研究的目的:
- 开发一个创新的生命周期数字双胞胎模型和RUL预测框架.
- 为了应对有限的工业数据对RUL预测的挑战.
- 为了提高复杂设备中RUL预测的准确性和可靠性.
主要方法:
- 作为数字表示,创建了一个轴承的六度自由度动态模型.
- 对于一个全生命周期的动态模型,KAN映射网络预测了轴承参数演变.
- 与CycleGAN集成的自我组织的神经操作员通过虚拟物理域互动实现了双胞胎信号的代更新.
主要成果:
- 生成的生命周期双胞胎数据与测量数据分布具有很高的相似性和一致性.
- 拟议的数字双胞胎模型有效地解决了数据不足的问题.
- 即使使用有限的生命周期数据,也可以实现准确的RUL预测.
结论:
- 开发的生命周期数字双胞胎模型和RUL预测框架有效地克服了数据限制.
- 该方法显示与先进的RUL预测模型兼容.
- 这种方法提高了工业系统智能操作和维护的潜力.
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