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研究一种基于声辐射传感器系统的螺栓腐蚀分类方法
Shuyi Di1, Yin Wu1, Yanyi Liu1
1College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
本研究介绍了一种无线声波发射传感器网络 (WASN),用于准确分类螺栓腐蚀水平. 该系统的准确性超过98%,提高了关键基础设施的安全性.
科学领域:
- 结构健康监测 结构健康监测
- 材料科学 材料科学 材料科学
- 信号处理 信号处理
背景情况:
- 高强度螺栓是桥梁和铁路等基础设施的关键组成部分.
- 监测螺栓完整性对于防止事故和促进维修至关重要.
- 腐蚀显著降低了螺栓的性能,并造成了安全风险.
研究的目的:
- 开发和验证一种用于准确检测和分类螺栓腐蚀水平的新型系统.
- 通过有效的螺栓状况监测,提高超高压设备的可靠性和安全性.
- 建立一个可靠的方法来评估螺栓的降解,使用声辐射信号.
主要方法:
- 无线声波发射传感器网络 (WASN) 的实施,用于高速信号采集.
- 应用ReliefF算法,从声辐射数据中选择最佳特征.
- 使用极端学习机器 (ELM) 模型进行腐蚀分类.
- 使用改进的算法 (GOOSE) 优化ELM参数,以提高预测准确度.
主要成果:
- 在五个不同的螺栓腐蚀水平 (0%到100%) 上进行实验验证.
- 实现了98.04%的最低分类准确度,用于检测螺栓腐蚀.
- 与现有的分类诊断模型相比,证明了卓越的性能.
- 在不同的工作条件下表现出强大的概括能力.
结论:
- 拟议的基于WASN的系统为螺栓腐蚀分类提供了一个高度准确和可靠的方法.
- 整合ReliefF和GOOSE优化的ELM提供了强大的AE信号识别.
- 这种方法对关键基础设施的结构健康监测和安全做出了重大贡献.
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