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基于维格纳-威利变换的车辆耐久性疲劳载荷谱加速编辑方法

Accelerated Editing Method for Vehicle Durability Fatigue Load Spectrum Based on Wigner-Ville Transform.

作者信息

Yang Yongle, Zhang Zhifei, Peng Liangfeng, Jin Jie, Wang Qinghua

机构信息

College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400030, China.

Research Institute of Highway Ministry of Transport, Beijing 100088, China.

出版信息

Sensors (Basel). 2023 Jul 16;23(14):6435. doi: 10.3390/s23146435.

DOI:10.3390/s23146435
PMID:37514729
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10385287/
Abstract

A Wigner-Ville transform-based (WVT-based) load spectrum fast editing method for vehicle parts is proposed to improve the efficiency of durability tests. In this method, the instantaneous energy spectrum (IES) of the original time-domain signal is obtained via the Wigner-Ville transform, which is used as a criterion to identify time-domain points of ineffective damage contribution. A genetic algorithm (GA) based threshold optimization model is also proposed to automatically set the threshold of the IES under consideration of the relative damage requirements and statistical parameters of the signal. The effectiveness of the above proposed editing method is demonstrated by compiling an SUV's suspension coil spring signal obtained from physical sensor-based measurements. Meanwhile, the same spectrum is also processed using time-domain editing, Short-time Fourier-transform, and S-transform methods for comparison. The results show that the WVT-based edited spectrum has a time-duration retention ratio of about 76.30%, which is significantly superior to other methods, with the same pseudo-damage retention and statistical parameter error constraints. Moreover, in combination with the fatigue simulation analysis, it verifies that the load effect of the edited spectrum matches well with that of the original. Thus, the proposed method is considered more effective for compiling component load signals in vehicle acceleration durability tests.

摘要

为提高耐久性试验效率,提出一种基于维格纳-威利变换(WVT)的车辆零部件载荷谱快速编辑方法。该方法通过维格纳-威利变换获取原始时域信号的瞬时能量谱(IES),以此作为识别无效损伤贡献时域点的准则。还提出一种基于遗传算法(GA)的阈值优化模型,在考虑信号相对损伤要求和统计参数的情况下自动设置IES的阈值。通过编辑基于物理传感器测量获得的某款SUV悬架螺旋弹簧信号,验证了上述编辑方法的有效性。同时,还使用时域编辑、短时傅里叶变换和S变换方法对相同频谱进行处理以作比较。结果表明,在相同的伪损伤保留和统计参数误差约束下,基于WVT编辑的频谱时间持续保留率约为76.30%,明显优于其他方法。此外,结合疲劳仿真分析,验证了编辑后频谱的载荷效应与原始频谱匹配良好。因此,该方法在车辆加速耐久性试验中编辑零部件载荷信号方面更有效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/e84a6097122b/sensors-23-06435-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/9599ed337d2c/sensors-23-06435-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/286d243671ee/sensors-23-06435-g007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/4fc5d6882771/sensors-23-06435-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/6e91b00b3306/sensors-23-06435-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/a074a7aaf070/sensors-23-06435-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/62e8c3d918a6/sensors-23-06435-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/5594066d908f/sensors-23-06435-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/9e8f19515063/sensors-23-06435-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/e84a6097122b/sensors-23-06435-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/9599ed337d2c/sensors-23-06435-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/52706f2cd4e8/sensors-23-06435-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/d67a7862230c/sensors-23-06435-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/3a9684685b9b/sensors-23-06435-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/7bfde9abdd48/sensors-23-06435-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/286d243671ee/sensors-23-06435-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/449ff75b3a94/sensors-23-06435-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/4fc5d6882771/sensors-23-06435-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/6e91b00b3306/sensors-23-06435-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/a074a7aaf070/sensors-23-06435-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/62e8c3d918a6/sensors-23-06435-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/5594066d908f/sensors-23-06435-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/9e8f19515063/sensors-23-06435-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/191e/10385287/e84a6097122b/sensors-23-06435-g015.jpg

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