Abstract: The quality of road load spectrum data is crucial for vehicle reliability research and development. This paper systematically expounds core processing methods based on engineering practice, typical working conditions are screened through multi-dimensional user surveys (driving routes, load, failure modes, etc.) and big data technology to determine acquisition routes. Measurement points are selected by balancing sensitivity and economy, covering key positions such as wheels, frames, and cabs to collect multi-type load signals. Sampling frequency setting is optimized based on the Nyquist sampling theorem and engineering experience, with anti-aliasing filters configured and signal integrity verified. In the preprocessing stage, data is purified through high-pass filtering (zero drift correction), shear replacement (spike correction), and physical rule verification (multi-signal correlation validation) to ensure signal consistency and usability. This study provides a replicable solution for load spectrum acquisition under complex working operating conditions, contributing to improve data reliability and vehicle research and development efficiency.

Key words: road load spectrum acquisition; sampling frequency; high pass filtering; physical rule verification

摘要： 道路载荷谱数据质量对车辆可靠性研发至关重要。文章结合工程实践系统阐述其核心 处理方法，通过多维度用户调查（行车路线、载重、失效模式等）及大数据技术筛选典型工 况，确定采集路线；测点选择兼顾敏感性与经济性，覆盖车轮、车架、驾驶室等关键位置， 采集多类型载荷信号；基于奈奎斯特采样定理与工程经验，优化采样频率设置，配置抗混叠 滤波器并验证信号完整性；在预处理环节通过高通滤波（零漂处理）、剪切替换（毛刺修正） 及物理规则校验（多信号关联性验证）净化数据，确保信号一致性与可用性。文章为研究复 杂工况下载荷谱采集提供可复制方案，助力提升数据可靠性与车辆研发效率。

关键词: 道路载荷谱采集；采样频率；高通滤波；物理规则校验