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在电梯系统上使用深度学习算法和多体动力学模型检测和识别损坏
Josef Koutsoupakis1, Dimitrios Giagopoulos1, Panagiotis Seventekidis1
1School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
这项研究引入了一种新的方法,用于使用深度学习和振动数据来检测电梯损坏. 该方法将模拟数据与现实世界的测量相结合,用于准确的结构健康监测.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 结构健康监测 结构健康监测
背景情况:
- 及时检测机械系统的损坏对于防止灾难性故障和优化维护计划至关重要.
- 现有的信号分析方法用于异常检测,往往与稀缺或不存在的数据作斗争.
- 电梯系统需要强大的结构健康监测,以确保安全和运营效率.
研究的目的:
- 开发一种用于电梯系统损坏检测和识别的新方法.
- 为了应对现实应用中有限的振动数据的挑战.
- 为电梯创建一个有效的结构健康监测工具.
主要方法:
- 将物理振动测量与高保真多体动态模型相结合.
- 使用深度学习算法,包括一组自动编码器和卷积神经网络.
- 通过多体动态模拟生成高质量的训练数据.
- 开发和整合一个专门的数据采集系统与电梯.
主要成果:
- 开发的框架准确地识别了电梯系统中的损坏.
- 该方法表明了有效监测条件的潜力.
- 该方法成功地训练深度学习模型,使用模拟和现实数据的组合.
结论:
- 这种新的方法为电梯系统的结构健康监测提供了一个强大的工具.
- 这种方法可以显著减少手工损坏定位所需的时间和精力.
- 模拟数据和深度学习的整合提高了在数据稀缺环境中损害检测能力.
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