码头集装箱起重机的结构损坏早期预警方法基于模糊的热率变化偏差偏差
Jiahui Liu1, Jian Zhao1, Dong Zhao1
1School of Technology, Beijing Forestry University, Beijing 100083, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究引入了一种新方法,用于早期预警码头集装箱起重机 (QCC) 的结构损坏,使用模糊率变化偏差 (FERVD). 通过FERVD方法,即使有不完整的数据,也可以提供及时的警报,从而提高结构健康监测 (SHM).
科学领域:
- 结构工程 结构工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 实时结构健康监测 (SHM) 和预警系统对于安全和维护至关重要.
- 码头集装箱起重机 (QCC) 需要强大的方法来检测异常状态,特别是在不完整的损坏数据的情况下.
研究的目的:
- 利用模糊率变化偏差 (FERVD) 开发QCC中结构异常状态的及时早期预警方法.
- 改进QCC的结构健康评估和安全维护.
主要方法:
- 应用双树复杂波波变换 (DTCWT) 来监测适应频段分解的数据.
- 使用模糊 (FE) 来构建FERVD指示器的提取响应信号特征.
- 建立基于FERVD指标的早期预警的动态值.
- 为QCC开发有限元模型 (FEM),模拟各种损坏场景.
主要成果:
- 该FERVD指标有效地区分了健康的结构状态和各种损伤条件.
- 数字实验和案例研究证实了该方法能够为不同损坏状态提供及时警告的能力.
- FERVD值保持在健康结构的值范围内,验证了其在QCC SHM中的适用性.
结论:
- 拟议的基于FERVD的方法为QCC中的结构异常状态早期预警提供了及时有效的解决方案.
- 该方法在现实世界结构性健康监测场景中证明了其稳定性和适用性.
- 这种方法增强了关键基础设施 (如QCC) 的安全和维护策略.
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