Research on rail fastener failure diagnosis method based on LabVIEW
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摘要: 扣件是联结钢轨与支承结构的重要部件。为便于研究扣件失效诊断方法，基于LabVIEW开发一套集信号采集、存储和分析于一体的轨道扣件失效诊断系统，采用NI-cRIO9056机箱和NI-9234电压模块作为硬件部分采集振动信号。针对扣件失效特征频率难以提取的问题，利用小波包和希尔伯特(Hilbert)包络谱相结合的方法，首先将采集的振动信号进行小波包分解，对能量变化较大的频段进行信号重构，最后利用希尔伯特包络谱图识别扣件状态。实验结果表明，该方法能够准确诊断扣件失效。
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关键词:
- LabVIEW 软件 /
- 扣件失效 /
- 诊断方法 /
- 小波包分析 /
- 希尔伯特包络谱
Abstract: Fasteners are important components connecting the rail and the supporting structure. To facilitate research on fastener failure diagnosis methods, a track fastener failure diagnosis system integrating signal acquisition, storage, and analysis was developed based on LabVIEW. The NI-cRIO9056 chassis and the NI-9234 voltage module were used as the hardware to acquire the vibration signals. To address the difficulty in extracting the characteristic frequency of fastener failure, a method combining wavelet packet and Hilbert envelope spectrum was proposed. The acquired vibration signals were first subjected to wavelet packet decomposition, and signals in the frequency bands with large energy changes were reconstructed. Finally, the Hilbert envelope spectrum was used to identify the fastener status. Experimental results show that this method can accurately diagnose fastener failure. -
图 4 小波包分析及频带能量分析程序框图
Figure 4. Block diagram of wavelet packet analysis and frequency band energy analysis program
图 6 钢轨在两种扣件状态下的时域振动波形
Figure 6. Time-domain vibration waveforms of rails in two fastener states
图 7 钢轨在两种扣件状态下的频域振动波形
Figure 7. Frequency-domain vibration waveforms of rails in two fastener states
图 10 小波包重构信号的包络谱图
Figure 10. Envelope spectrum of wavelet packet reconstruction signal
表 1 频带的频率范围
Table 1. Frequency range of frequency band
单位：Hz w7 w8 w9 w10 w11 w12 w13 w14 0～103 103～206 206～309 309～412 412～515 515～618 618～721 721～824 
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[1] 杨伟挺, 王安斌, 高晓刚, 等. 城市轨道交通不同车速引起地面振动特性研究[J] . 物流科技, 2022, 45(13): 80 − 84. doi: 10.13714/j.cnki.1002-3100.2022.13.020 [2] 程保青, 杨其振, 刘道通. 城轨交通新型轨道扣件研究与设计[J] . 铁道工程学报, 2012(4): 90 − 94. doi: 10.3969/j.issn.1006-2106.2012.04.017 [3] 余自若, 袁媛, 张远庆, 等. 高速铁路扣件系统弹条疲劳性能研究[J] . 铁道学报, 2014, 36(7): 90 − 95. doi: 10.3969/j.issn.1001-8360.2014.07.015 [4] 孙旭, 王平. 高速铁路扣件失效对车辆-轨道耦合系统动态响应的影响[J] . 铁道学报, 2022, 44(8): 108 − 116. doi: 10.3969/j.issn.1001-8360.2022.08.012 [5] 詹彩娟. 地铁用新型中等减振扣件系统动力学仿真分析及减振性能测试[J] . 噪声与振动控制, 2020, 40(2): 230 − 235, 258. doi: 10.3969/j.issn.1006-1355.2020.02.042 [6] LI Y, TRINH H, HAAS N, et al. Rail component detection, optimization, and assessment for automatic rail track inspection[J] . IEEE Transactions on Intelligent Transportation Systems, 2014, 15(2): 760 − 770. doi: 10.1109/TITS.2013.2287155 [7] 刘福政, 高军伟. 基于LabVIEW和cRIO的滚动轴承故障诊断系统设计[J] . 仪表技术与传感器, 2021(4): 67 − 70. doi: 10.3969/j.issn.1002-1841.2021.04.014 [8] DE ASMUNDIS R. LabVIEW - A flexible environment for modeling and daily laboratory use[M] . London: IntechOpen, 2021. [9] 沙云东, 陈兴武, 栾孝驰, 等. 基于小波包分解-峭度值指标-希尔伯特包络解调融合方法处理声发射信号的滚动轴承故障诊断[J] . 科学技术与工程, 2023, 23(21): 9315 − 9323. doi: 10.3969/j.issn.1671-1815.2023.21.049 [10] MALIUK A S, AHMAD Z, KIM M J. A technique for bearing fault diagnosis using novel wavelet packet transform-based signal representation and informative factor LDA[J] . Machines, 2023, 11(12): 1080. doi: 10.3390/machines11121080 [11] 周文静, 刘冲, 韦佳宏, 等. 基于小波包能量谱的钢轨扣件松脱检测研究[J] . 传感器与微系统, 2017, 36(5): 40 − 43. doi: 10.13873/J.1000-9787(2017)05-0040-04 [12] 张丽华. 钢轨扣件松脱检测信号的HHT分析方法[D] . 大连: 大连理工大学, 2014. -
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