基于机器学习的铁路波纹识别:地铁车辆的响应和噪音视角
Xiaopei Cai1, Xueyang Tang1, Wenhao Chang1
1Beijing Jiaotong University, People's Republic of China.
概括
一个新的粒子概率神经网络 (PPNN) 算法通过分析车载噪音和车加速,有效地识别铁路波纹. 这种人工智能方法在检测铁路波长和振幅方面实现了高精度,改善了地铁线路维护.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 铁路波纹是地铁系统中普遍存在的问题,影响运营效率和维护.
- 准确识别铁路波纹的波长和振幅对于及时干预至关重要.
研究的目的:
- 开发一种有效的算法来识别铁路波纹波长和振幅.
- 为了利用车载噪声和车加速数据进行波纹分析.
主要方法:
- 开发了一种粒子概率神经网络 (PPNN) 算法,集成了粒子群优化和概率神经网络.
- 车载噪声特征 (特定频率的声压水平) 用于识别轨道波长 (30和50毫米).
- 通过完整集体实证模式分解与自适应噪声处理的波吉加速数据被用于识别轨道振幅 (0.1和0.2毫米).
主要成果:
- 在识别使用车载噪声的铁路波长方面,PPNN算法实现了96.43%的平均准确性.
- 该PPNN算法实现了95.40%的平均准确度在识别轨道幅度使用车加速能量.
- 为了有效的功能选择,开发了一种逐步移动的窗口搜索算法.
结论:
- 开发的PPNN算法提供了一个非常准确和高效的方法来检测铁路波纹参数.
- 这种由人工智能驱动的方法显示了改善地铁基础设施监测和维护的巨大潜力.
- 该研究强调了整合声学和动态测量的有效性,用于诊断铁路缺陷.
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